<?xml version="1.0" encoding="UTF-8"?>
<quiz>
<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik</text>
    </category>
    <info format="markdown">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Kreisbewegung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 7983  -->
  <question type="formulas">
    <name>
      <text>Frequenz und Winkelgeschwindigkeit von Uhrzeigern</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Mit welcher {nameFreq} bewegt sich der {nameHand} einer Uhr?</p>

<br><p>
<img src="@@PLUGINFILE@@/Armbanduhr.jpg" alt="" width="150">
</p><br>]]></text>
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</file>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jFreq = {0:2};
jHand = {0:3};</text>
</varsrandom>
<varsglobal><text><![CDATA[nameHands = ["Stundenzeiger", "Minutenzeiger", "Sekundenzeiger"];
factorHands = [43200, 3600, 60];
nameFreqs = ["Frequenz", "Winkelgeschwindigkeit"];
factorFreqs = [1, 6.283185];
unitFreqs = ["Hz", "rad/s"];

nameHand = nameHands[jHand];
factorHand = factorHands[jHand];
nameFreq = nameFreqs[jFreq];
factorFreq = factorFreqs[jFreq];
unitFreq = unitFreqs[jFreq];

solFreq = factorFreq / factorHand;]]></text>
</varsglobal>
<answernumbering><text>abc</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = solFreq;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}
nSigFigAns = max( nSigFigAns , 2 );

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 0.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.7*critMantRnd1, 0.4*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>&emsp;{_0}&ensp;\( \mathrm{{unitFreq}} \) &emsp; (Angabe mit {nSigFig} signifikanten Stelle(n) ohne Einheit)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 7984  -->
  <question type="formulas">
    <name>
      <text>Kräfteverhältnis bei Kugeln auf rotierenden Scheiben</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Auf den horizontalen Scheiben des dargestellten Apparats kreisen bunte Kugeln.</p>

<br><p><img src="@@PLUGINFILE@@/tikz_BunteKugelnAufScheiben.jpg" alt="" width="350" height="178" role="presentation" class="img-responsive atto_image_button_middle"></p><br>]]></text>
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    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jShuffle = shuffle([0,1,2]);</text>
</varsrandom>
<varsglobal><text><![CDATA[colorNames = ["rot" , "grün", "blau"];
factors = [4, 2, 1];


j1 = jShuffle[0];
j2 = jShuffle[1];

col1 = colorNames[j1];
col2 = colorNames[j2];

sol = factors[j1] / factors[j2];]]></text>
</varsglobal>
<answernumbering><text>abc</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>In welchem Verhältnis stehen die Zentripetalkräfte auf die {col1}en und die {col2}en Kugeln zueinander?</p>
<p>&emsp; \( \dfrac{ F_\mathrm{Z,{col1}} }{ F_\mathrm{Z,{col2}} } =\; \) {_0}&emsp; (Geben Sie eine Dezimalzahl ein.)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
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<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 7985  -->
  <question type="formulas">
    <name>
      <text>Resultierende beim Hammerwurf</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Zum Aufwärmen schleudert ein Hammerwerfer eine Kugel von \( {mass}\,\mathrm{kg} \) an einem circa \( 1.2\,\mathrm{m} \) langen Seil horizontal im Kreis. Zuzüglich seiner Armlänge ergibt das einen Bahnradius von \( {radius}\,\mathrm{cm} \).</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>mass = {5.14:9:0.2};
radius = {151:171:2};
speed = {24.7:32:1};</text>
</varsrandom>
<varsglobal><text>FZ = mass * speed**2 / ( radius * 10**(-2) );</text>
</varsglobal>
<answernumbering><text>abc</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = FZ;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1, 0.3*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.15</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>N: G M;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie gross ist die Resultierende auf die Kugel bei einer Bahngeschwindigkeit von \( {speed}\,\mathrm{m/s} \)?</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 7986  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung Zentripetalkraft (einfach)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper bewege sich auf einer Kreisbahn. In den folgenden Teilaufgaben wird gedanklich jeweils ein Parameter variiert, während alle anderen konstant gehalten werden.</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>5.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jAngularVelocity = {0:8};
jOrbitalSpeed = {0:8};
jMass = {0:8};
jRadius = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "ein Fünftel" , "ein Viertel" , "ein Drittel" , "halb" , "doppelt" , "dreimal" , "viermal" , "fünfmal" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];

strAngularVelocity = factNames[jAngularVelocity];
solAngularVelocity = sigfig( (factNumbers[jAngularVelocity])**(2) ,2);

strOrbitalSpeed = factNames[jOrbitalSpeed];
solOrbitalSpeed = sigfig( (factNumbers[jOrbitalSpeed])**(2) ,2);

strMass = factNames[jMass];
solMass = sigfig( factNumbers[jMass] ,2);

strRadius = factNames[jRadius];
solRadiusConstAngularVelocity = sigfig( (factNumbers[jRadius])**(+1) ,2);
solRadiusConstOrbitalSpeed = sigfig( (factNumbers[jRadius])**(-1) ,2);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solMass</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor würde sich der Betrag der Resultierenden unterscheiden, wenn <u>die Masse</u> des Körpers {strMass} so gross wäre?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solAngularVelocity</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor würde sich der Betrag der Resultierenden unterscheiden, wenn  <u>die Winkelgeschwindigkeit</u> {strAngularVelocity} so gross wäre?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solOrbitalSpeed</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor würde sich der Betrag der Resultierenden unterscheiden, wenn  <u>die Bahngeschwindigkeit</u> {strOrbitalSpeed} so gross wäre?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solRadiusConstAngularVelocity</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor würde sich der Betrag der Resultierenden unterscheiden, wenn  <u>der Radius bei gleicher Winkelgeschwindigkeit</u> {strRadius} so gross wäre?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>4</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solRadiusConstOrbitalSpeed</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor würde sich der Betrag der Resultierenden unterscheiden, wenn  <u>der Radius bei gleicher Bahngeschwindigkeit</u> {strRadius} so gross wäre?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 7987  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung Zentripetalkraft (schwer)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Auf einen Körper wirke eine bestimmte Zentripetalkraft, sodass er sich auf einer gegebenen Kreisbahn bewegt. Diese Situation ist der Ausgangspunkt für alle folgenden Teilaufgaben.</p>

<p>Der Fokus liegt dabei in der Verhältnisbildung für hypothetische Änderungen, nicht darauf, wie diese Änderungen realistischerweise stattfinden könnten.</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jAVOS = {0:8};
bAVorOS = {0:2};
jMass = {0:8};
jRadius = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "gefünftelt" , "geviertelt" , "gedrittelt" , "halbiert" , "verdoppelt" , "verdreifacht" , "vervierfacht" , "verfünffacht" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];

strAVorOS = ["Winkelgeschwindigkeit" , "Bahngeschwindigkeit" ][bAVorOS];
strMass = factNames[jMass];
strRadius = factNames[jRadius];
strAVOS = factNames[jAVOS];

sol = sigfig( factNumbers[jMass] * factNumbers[jRadius]**((bAVorOS==0) ? (+1) : (-1)) * (factNumbers[jAVOS])**(2) ,2);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p dir="ltr" style="text-align: left;">Um welchen Faktor ändert sich die Zentripetalkraft, wenn die Masse des Körpers&nbsp;{strMass}, die {strAVorOS} {strAVOS} und dabei der Radius {strRadius} wird?<br>{_0} (Dezimalzahl eingeben)<br></p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Gravitation</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8011  -->
  <question type="formulas">
    <name>
      <text>Gravitationsgesetz: Abstand berechnen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Welchen Abstand haben zwei kugelförmige Körper mit {m1Str} und {m2Str} Masse, wenn sie sich mit einer Gravitationskraft von {FGStr} anziehen?</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m1Mant = {100.1:1000:0.2};
m1Exp = {3:30};

m2Mant = {100.1:1000:0.2};
m2Exp = {3:30};

FGMant = {100.1:1000:0.2};
FGExpJ = {3:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[Prefixes = ["p", "n", "µ", "m", "c", "d", "da", "h", "k", "M", "G", "T"];
Exponents = [-12, -9, -6,-3, -2, -1, 1, 2, 3, 6, 9, 12]; 

G = 6.674e-11;

m1Val = m1Mant * 10**(m1Exp);
m1Str = join(" " , "\\(" , str(m1Mant) , "{\\cdot} 10^{" , str(m1Exp) , "}\\,\\mathrm{kg}\\)" );

m2Val = m2Mant * 10**(m2Exp);
m2Str = join(" " , "\\(" , str(m2Mant) , "{\\cdot} 10^{" , str(m2Exp) , "}\\,\\mathrm{kg}\\)" );

FGVal = FGMant * 10**(Exponents[FGExpJ]);
FGStr = join(" " , "\\(" , str(FGMant) , "\\,\\mathrm{" , Prefixes[FGExpJ] , "N}\\)" );

r =  sqrt( G * m1Val * m2Val / FGVal );]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 4;
origSol = r;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1, 0.3*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>m: P T G M k d c m n;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8012  -->
  <question type="formulas">
    <name>
      <text>Gravitationsgesetz: Kraft berechnen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Diese Aufgabe ist rein akademisch in dem Sinne, dass sie eine Rechenübung darstellt und keinen Bezug zu realistischen Grössenangaben hat.</p>
<p>Berechnen Sie die gesuchte Grösse und geben Sie sie mit richtiger Syntax samt Einheit an.</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m1Mant = {100.1:1000:0.2};
m1Exp = {3:30};

m2Mant = {100.1:1000:0.2};
m2Exp = {3:30};

rMant = {100.1:1000:0.2};
rExpJ = {7:12};</text>
</varsrandom>
<varsglobal><text><![CDATA[Prefixes = ["p", "n", "µ", "m", "c", "d", "da", "h", "k", "M", "G", "T"];
Exponents = [-12, -9, -6,-3, -2, -1, 1, 2, 3, 6, 9, 12]; 

G = 6.674e-11;

m1Val = m1Mant * 10**(m1Exp);
m1Str = join(" " , "\\(" , str(m1Mant) , "\\cdot 10^{" , str(m1Exp) , "}\\,\\mathrm{kg}\\)" );

m2Val = m2Mant * 10**(m2Exp);
m2Str = join(" " , "\\(" , str(m2Mant) , "\\cdot 10^{" , str(m2Exp) , "}\\,\\mathrm{kg}\\)" );

rVal = rMant * 10**(Exponents[rExpJ]);
rStr = join(" " , "\\(" , str(rMant) , "\\,\\mathrm{" , Prefixes[rExpJ] , "m}\\)" );

FGr = G * m1Val * m2Val * rVal**(-2);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 4;
origSol = G * m1Val * m2Val * rVal**(-2);
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie gross ist die Gravitationskraft auf einen kugelförmigen Körper von {m1Str}, wenn er sich in einem Abstand von {rStr} von einem anderen kugelförmigen Körper von {m2Str} befindet?</p>
<p>&emsp;{_0}{_u}</p><br>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8013  -->
  <question type="formulas">
    <name>
      <text>Gravitationsgesetz: Masse berechnen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Dieses Aufgabe ist rein akademisch in dem Sinne, dass sie eine Rechenübung darstellt und keinen Bezug zu realistischen Grössenangaben hat.</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m1Mant = {100.1:1000:0.2};
m1Exp = {3:30};

FGMant = {100.1:1000:0.2};
FGExpJ = {3:8};

rMant = {100.1:1000:0.2};
rExpJ = {7:12};</text>
</varsrandom>
<varsglobal><text><![CDATA[Prefixes = ["p", "n", "µ", "m", "c", "d", "da", "h", "k", "M", "G", "T"];
Exponents = [-12, -9, -6,-3, -2, -1, 1, 2, 3, 6, 9, 12]; 

G = 6.674e-11;

m1Val = m1Mant * 10**(m1Exp);
m1Str = join(" " , "\\(" , str(m1Mant) , "\\cdot 10^{" , str(m1Exp) , "}\\,\\mathrm{kg}\\)" );

FGVal = FGMant * 10**(Exponents[FGExpJ]);
FGStr = join(" " , "\\(" , str(FGMant) , "\\,\\mathrm{" , Prefixes[FGExpJ] , "N}\\)" );

rVal = rMant * 10**(Exponents[rExpJ]);
rStr = join(" " , "\\(" , str(rMant) , "\\,\\mathrm{" , Prefixes[rExpJ] , "m}\\)" );

sol = FGVal / G / m1Val * rVal**(2);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 4;
origSol = sol;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1, 0.3*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>kg</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welche Masse hat ein kugelförmiger Körper, wenn auf ihn in einem Abstand von {rStr} von einem anderen kugelförmigen Körper von {m1Str} eine Gravitationskraft von {FGStr} wirkt?</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8014  -->
  <question type="formulas">
    <name>
      <text>Gravitationspotential einstellen (Verhältnisbildung)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Im Diagramm markiert der blaue Punkt die potenzielle Energie einer Raumsonde in 10 Millionen Kilometern Abstand vom Erdmittelpunkt. Wie üblich liegt der Nullpunkt der potenziellen Energie im Unendlichen.</p>

<p>Verschieben Sie die beiden roten Punkte zu den korrekten Energiewerten, sodass die entstehende Kurve durch die drei Punkte den richtigen Verlauf der potenziellen Energie in Abhängigkeit des Abstandes wiedergibt.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrRs = shuffle([20, 30, 40, 50, 60]);</text>
</varsrandom>
<varsglobal><text>r2 = min( arrRs[0], arrRs[1]);
r3 = max( arrRs[0], arrRs[1]);

Epot1 = -60;
Epot2 = Epot1 / ( r2 / 10 );
Epot3 = Epot1 / ( r3 / 10 );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>2</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[Epot2, Epot3]</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="800" height="600" numberOfBoards="1" ext_formulas>
var jsxCode = function (question) {


//-- Settings and general definitions

const bDraft = false;
const questionIsSolved = (typeof question === 'undefined') ? false : question.isSolved;

JXG.Options.elements.highlight = false;
JXG.Options.point.snapToGrid = true;
JXG.Options.point.snapSizeX = 1;
JXG.Options.point.snapSizeY = 1;
JXG.Options.point.showInfobox = true;
JXG.Options.point.infoboxDigits = 0;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

const colRed = '#F44336', colPink = '#E91E63', colPurple = '#9C27B0', colBlue = '#2196F3',
	colIndigo = '#3F51B5', colCyan = '#00BCD4', colGreen = '#4CAF50', colYellow = '#FFEB3B',
	colAmber = '#FFC107', colOrange = '#FF9800', colBrown = '#795548', colGrey = '#9E9E9E';


//-- Set parameters for before and after submission

const t1 = 10, t2 = {r2}, t3 = {r3}, x1 = {Epot1};
var x2, x3;
[x2,x3] = question.getAllValues([x1+5,x1+6]);


//-- Create boards

const brds = question.initBoards([{
	boundingbox: [-5, 12, 67, -110], axis: false
}]);
const [brd0] = brds;

if (bDraft) console.log(brd0);


//-- Draw axes

brd0.create('axis', [[0, 0], [65, 0]], {
	straightFirst: false, straightLast: false,
	withLabel: true, name: 'r (10^6 m)',
	label: { position: 'rt', offset: [3, 15], anchorX: 'right', parse: true, fontSize: 12 },
	ticks: {
		drawZero: false, insertTicks: false,
		ticksDistance: 10, majorHeight: () => 100 * 2 * brd0.unitY, 
		minorTicks: 0, minorHeight:  () => 100 * 2 * brd0.unitY, tickEndings: [0, 1],
		label: { visible: true, anchorX: 'middle', anchorY: 'top', offset: [0, -4] }
	}
});
brd0.create('axis', [[0, -105], [0, 8]], {
	straightFirst: false, straightLast: false,
	withLabel: true, name: 'E_{pot} (MJ)',
	label: { position: 'rt', offset: [10, -5], anchorX: 'left', parse: true, fontSize: 12 },
	ticks: {
		drawZero: true, insertTicks: false, 
		ticksDistance: 10, majorHeight: () => 60 * 2 * brd0.unitX,
		minorTicks: 4, minorHeight: 0, tickEndings: [0, 1],
		label: { visible: true, anchorX: 'right', anchorY: 'middle', offset: [-5, 0] }
	}
});



//-- Graphical elements on the boards

function attrPmov(addAttr = {}) {
	const attr = { fixed: questionIsSolved, withLabel: false, color: colRed };
	//   const attr = {fixed: question.isSolved, withLabel: false};
	return { ...attr, ...addAttr };
}
function attrPsma(addAttr = {}) {
	const attr = { visible: true, withLabel: false, color: colBlue, strokeWidth: 0, size: 0.5 };
	return { ...attr, ...addAttr };
}
const attrLine = { borders: { strokeColor: colBlue, strokeWidth: 3 } };

var pX1 = brd0.create('point', [t1, x1], {fixed: true, withLabel: false, color: colBlue }),
	pX2 = brd0.create('point', [t2, x2], attrPmov({  })),
	pX3 = brd0.create('point', [t3, x3], attrPmov({  }));

function g12(x) {
	return (pX2.Y()-pX1.Y()) / (pX2.X()-pX1.X()) * (x-pX1.X()) + pX1.Y();
}
function g13(x) {
	return (pX3.Y()-pX1.Y()) / (pX3.X()-pX1.X()) * (x-pX1.X()) + pX1.Y();
}
pX2.on('drag', function () { 
	this.moveTo([t2, Math.max(Math.min(this.Y(),-1),x2)], 0);
	pX3.moveTo([t3, Math.max(Math.min(pX3.Y(),Math.ceil(g12(t3)-1)),this.Y()+1)], 0);
	calcParams(); });
pX3.on('drag', function () { 
	this.moveTo([t3, Math.max(Math.min(this.Y(),0),x3)], 0);
	pX2.moveTo([t2, Math.max(Math.min(pX2.Y(),this.Y()-1),Math.floor(g13(t2)+1))], 0);
	calcParams(); });

var A, B, C;
function calcParams () {
	const x1 = pX1.X(), y1 = pX1.Y(),
			x2 = pX2.X(), y2 = pX2.Y(),
			x3 = pX3.X(), y3 = pX3.Y();
	A = -((x1-x2)*(x1-x3)*(x2-x3)*(y1-y2)*(y1-y3)*(y2-y3))/(x3*(-y1+y2)+x2*(y1-y3)+x1*(-y2+y3))/(x3*(-y1+y2)+x2*(y1-y3)+x1*(-y2+y3));
	B = (x1*x2*(-y1+y2)+x1*x3*(y1-y3)+x2*x3*(-y2+y3))/(x3*(y1-y2)+x1*(y2-y3)+x2*(-y1+y3));
	C = (x1*y1*(y2-y3)+x3*(y1-y2)*y3+x2*y2*(-y1+y3))/(x3*(y1-y2)+x1*(y2-y3)+x2*(-y1+y3));
}

calcParams();

brd0.create('functiongraph', [function (x) { return A/(x-B) + C; }, ()=> Math.max(0,B), 60], {
	strokeColor: colBlue, strokeWidth: 2, dash: 0
});

//-- Send parameters to Formulas

question.bindInput(0, () => { return pX2.Y(); });
question.bindInput(1, () => { return pX3.Y(); });

};
new JSXQuestion(BOARDIDS, jsxCode, allowInputEntry=false);

</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8015  -->
  <question type="formulas">
    <name>
      <text>Ortsfaktor auf einem Planeten</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Berechnen Sie anhand der Angaben in der Formelsammlung den Ortsfaktor auf der Oberfläche {strPlanet}. Verwenden Sie als Einheit \( \mathrm{N/kg} \).</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jPlanet = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[planetNames = ["des Merkur", "der Venus", "des Mars", "des Jupiter", "des Saturn", "des Uranus", "des Neptun", "des Mondes"];
planetMs = [0.331, 4.87, 0.642, 1898.5, 568.46, 86.818, 102.45, 0.07349];
planetRs = [2.425, 6.070, 3.395, 71.3, 60.1, 25.6, 24.3, 1.7375];

strPlanet = planetNames[jPlanet];
M = planetMs[jPlanet] * 10**(24);
R = planetRs[jPlanet] * 10**(6);

G = 6.673e-11;

g = G * M / (R**2);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = g;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1, 0.3*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N/kg</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>N: G M;</text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8016  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung Gravitationskraft (einfach)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Zwischen zwei gegebenen kugelförmigen Körpern wirke eine bestimmte Gravitationskraft. Diese ist der Ausgangspunkt für alle folgenden Teilaufgaben.</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jOneCharge = {0:8};
jDistance = {0:8};
jTwoCharge = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "gefünftelt" , "geviertelt" , "gedrittelt" , "halbiert" , "verdoppelt" , "verdreifacht" , "vervierfacht" , "verfünffacht" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];

solOneCharge = sigfig( factNumbers[jOneCharge] ,3);
strOneCharge = factNames[jOneCharge];

solDistance = sigfig( (factNumbers[jDistance])**(-2) ,3);
strDistance = factNames[jDistance];

solTwoCharge = sigfig( (factNumbers[jTwoCharge])**(2) ,3);
strTwoCharge = factNames[jTwoCharge];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solOneCharge</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die Kraft, wenn die Masse <u>eines Körpers</u> {strOneCharge} wird?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solDistance</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die Kraft, wenn <u>der Abstand</u> {strDistance} wird?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solTwoCharge</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die Kraft, wenn die Massen <u>beider Körper</u> {strTwoCharge} werden?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8017  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung Gravitationskraft (schwer)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Zwischen zwei gegebenen kugelförmigen Körpern wirke eine bestimmte Gravitationskraft. Diese ist der Ausgangspunkt für die folgende Aufgabe.<br></p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jChargeOne = {0:8};
jChargeTwo = {0:8};
jDistance = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "gefünftelt" , "geviertelt" , "gedrittelt" , "halbiert" , "verdoppelt" , "verdreifacht" , "vervierfacht" , "verfünffacht" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];

strChargeOne = factNames[jChargeOne];
strChargeTwo = factNames[jChargeTwo];
strDistance = factNames[jDistance];

sol = sigfig( factNumbers[jChargeOne] * factNumbers[jChargeTwo] * (factNumbers[jDistance])**(-2) ,2);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die Kraft, wenn die Masse des einen Körpers {strChargeOne}, die des anderen {strChargeTwo} und der Abstand {strDistance} wird?</p>
<p>&emsp;{_0} (Dezimalzahl eingeben)<br></p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8018  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung Gravitationskraft auf Satelliten</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Zwei Satelliten A und B gleicher Masse umkreisen die Erde auf kreisförmigen Umlaufbahnen. Der Abstand des Satelliten B vom Erdmittelpunkt ist {strDistance} so gross wie der des Satelliten A.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jDistance = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "ein Fünftel" , "ein Viertel" , "ein Drittel" , "halb" , "doppelt" , "dreimal" , "viermal" , "fünfmal" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];

solDistance = sigfig( (factNumbers[jDistance])**(-2) ,2);
strDistance = factNames[jDistance];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>solDistance</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>In welchem Verhältnis steht die Gravitationskraft auf B zur Gravitationskraft auf A?</p>
<p>&emsp;{_0}{_u}&emsp; (Dezimalzahl eingeben)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8019  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung Umlaufzeiten von Satelliten</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Zwei Satelliten kreisen um die Erde. Satellit 2 ist {=factNames[j]} so weit von der Erde entfernt wie Satellit 1, d.h. \(r_2/r_1={=factNumbers[j]}\).</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>j = {0:8};</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "ein Fünftel" , "ein Viertel" , "ein Drittel" , "halb" , "doppelt" , "dreimal" , "viermal" , "fünfmal" ];
factNumbers = [ "1/5" , "1/4" , "1/3" , "1/2" , "2" , "3" , "4" , "5" ];
factValues = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];

ratio = pow( factValues[j] , 2/3 );]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = ratio;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>In welchem Verhältnis stehen die Umlaufzeiten der Satelliten?</p>
<p>&emsp;\(T_2/T_1=\;\;\){_0}{_u}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Federkraft</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8246  -->
  <question type="formulas">
    <name>
      <text>Federkonstante aus F_F(y)-Diagramm bestimmen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Gegeben sei das folgende \(F_\mathrm{F}(y)\)-Diagramm für eine Hooke'sche Feder.<br></p>

<p><jsxgraph width="400" height="300">



var brd = JXG.JSXGraph.initBoard(BOARDID, {
    boundingbox: [-1,17, 12, -5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$y\\;\\mathrm{(cm)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$F_\\mathrm{F}\\;\\mathrm{(N)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 },
          ticks: {minorHeight:0 } } },
      showCopyright: false, showNavigation: false,
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false}
    });

brd.create('ticks', [brd.defaultAxes.y], { strokeColor:'#cccccc', strokeWidth:0.3,
      minorTicks:4, minorHeight:-1, majorHeight:0, ticksDistance:5 });

brd.create('functiongraph', [function(y){ return {D}*y;}, 0, 12] , {strokeColor:'#4285F4', strokeWidth:2});

</jsxgraph><br></p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>D = {0.2:1.6:0.1};</text>
</varsrandom>
<varsglobal><text></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = D;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 5.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1, 0.3*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.4*critExp + 0.6*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N/cm</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Bestimmen Sie die Federkonstante.</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit {nSigFig} signifikanten Stellen und Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8247  -->
  <question type="formulas">
    <name>
      <text>Federsystem – Einfache Berechnungen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Das abgebildete Federsystem besteht aus <em>drei gleichen</em> Federn mit Federkonstante {D} N/m. An der gelben Feder wird rechts mit einer Kraft \( \vec{F} \) gezogen. Dadurch&nbsp;ist die rote Feder um {yRot}.0 cm im Vergleich zu ihrer unbelasteten Länge gedehnt.</p>

<br><p><img src="@@PLUGINFILE@@/Federsystem.png" alt="" width="700" height="165" role="presentation" class="img-responsive atto_image_button_middle"></p><br>]]></text>
<file name="Federsystem.png" path="/" 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</file>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Die Antwort ist richtig.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Die Antwort ist teilweise richtig.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Die Antwort ist falsch.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>D = {113:789:18};

yRot = {11:30};</text>
</varsrandom>
<varsglobal><text>yGesamt = 3 * yRot;

FBetrag = 2 * D * yRot * 0.01;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = yGesamt;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.5*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.4*critExp + 0.1*critSigFig + 0.5*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>cm</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>N: M k h d c m;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um wie viel ist das gesamte Federsystem gegenüber seiner unbelasteten Länge gedehnt? [2P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = FBetrag;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
# critMant = max( 0.7*critMantCorrect, 0.6*critMantRnd1, 0.5*critMantRnd2 );
critMant = critMantCorrect;
grade = ( critMant > 0 ) ? 0.4*critExp + 0.1*critSigFig + 0.5*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welchen Betrag hat die Kraft \( \vec{F} \)? [2P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>Rechnung</text>
</tag>
      <tag><text>4P</text>
</tag>
    </tags>
  </question>

<!-- question: 8248  -->
  <question type="formulas">
    <name>
      <text>Federteilung – Einfache Berechnungen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p dir="ltr" style="text-align: left;">Die abgebildeten Federsysteme besteht aus drei gleichen Federn mit Federkonstante \( {D} \,\mathrm{N/m} \).&nbsp;<span style="font-size: 0.9375rem;">Beim oberen System (Federkonstante \( D_\mathrm{s} \)) sind drei Federn <u>seriell</u> aneinander gehängt.&nbsp;</span><span style="font-size: 0.9375rem;">Beim unteren System (Federkonstante \( D_\mathrm{p} \)) sind zwei Federn <u>parallel</u> verknüpft.</span></p><p><br></p><p></p><p dir="ltr" style="text-align: left;"><img src="@@PLUGINFILE@@/Federteilung.jpg" alt="" width="499" height="170" role="presentation" class="img-responsive atto_image_button_middle"><br></p><p dir="ltr" style="text-align: left;"><br></p>]]></text>
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</file>
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    <correctfeedback format="html">
      <text>Die Antwort ist richtig.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Die Antwort ist teilweise richtig.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Die Antwort ist falsch.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>D = {113:789:18};

FSerie = {11:25};</text>
</varsrandom>
<varsglobal><text>ySerie = sigfig( 3 * FSerie / D , 3 );

DParallel = 2 * D;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>ySerie</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.001]]></text>
 </correctness>
 <unitpenalty>
  <text>0.5</text>
 </unitpenalty>
 <postunit>
  <text>m</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p dir="ltr"><span>Am oberen System wird an beiden Enden mit \( {FSerie}.0 \,\mathrm{N} \) gezogen. Um wie viel ist es dabei gegenüber seiner unbelasteten Länge gedehnt?</span></p>
<p>{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>DParallel</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.5</text>
 </unitpenalty>
 <postunit>
  <text>N/m</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welchen Wert hat die Federkonstante des unteren Systems?</p>
<p>\( D_\mathrm{p} \;\;=\;\;\) {_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>6</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text>_err == 0</text>
 </correctness>
 <unitpenalty>
  <text>0.5</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor unterscheiden sich die Federkonstanten der beiden Systeme?</p>
<p>\( D_\mathrm{p}/D_\mathrm{s} \;\;=\;\;\) {_0}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
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 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>Rechnung</text>
</tag>
      <tag><text>5P</text>
</tag>
    </tags>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Gewichtskraft</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8278  -->
  <question type="formulas">
    <name>
      <text>Diagramm: F_G(t) aus Ortsfaktor</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Wir stellen uns einen Planeten mit Ortsfaktor \( {g} \,\mathrm{N/kg} \) vor.</p>

<p>Stellen Sie die Abhängigkeit der Gewichtskraft auf einen Körper von seiner Masse im \(F_\mathrm{G}(m)\)-Diagramm dar, indem Sie die drei Punkte vertikal verschieben.</p>
<br>]]></text>
    </questiontext>
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      <text></text>
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    <defaultgrade>3.0000000</defaultgrade>
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    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>g = {3.1:10:0.2};

m1 = {120:401:20};
m2 = {460:741:20};</text>
</varsrandom>
<varsglobal><text>F0 = 0;
F1 = round( m1 * g * 1e-3, 1);
F2 = round( m2 * g * 1e-3 , 1);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>3</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[F0,F1,F2]</text>
 </answer>
 <vars2>
  <text><![CDATA[crit_0 = ( abs( _0 - 0 ) < 0.1 );
crit_1 = ( abs( _1 - F1 ) < 0.1 );
crit_2 = ( abs( _2 - F2 ) < 0.1 );
crit_3 = crit_0 && ( abs( _2/m2 - _1/m1 ) < 0.001 );

crit_tot = ( 0.2*crit_0 + 0.4*crit_1 + 0.4*max(crit_2,crit_3) ) ;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="600" height="400" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.point.snapSizeX = 20;
  JXG.Options.point.snapSizeY = 0.1;
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  const t0 = 0, t1 = {m1}, t2 = {m2};
  var x0,x1,x2;
  [x0,x1,x2] = 
    question.getAllValues([1,1,1 ]);

  
  // Create boards
  var brd0 = question.initBoard(BOARDID0, { 
    boundingbox: [-50, 8, 800, -1], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$m\\;\\mathrm{(g)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$F_\\mathrm{G}\\;\\mathrm{(N/kg)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
      showCopyright: false, showNavigation: false, 
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false}
    });
    

  // Define lines and points on brd0
  // Q: Is it necessary/beneficial/wrong to suspendUpdate here?
  brd0.suspendUpdate();
  var lX0 = brd0.create('line', [[t0,-10], [t0,10]], attrGlid),
      lX1 = brd0.create('line', [[t1,-10], [t1,10]], attrGlid),
      lX2 = brd0.create('line', [[t2,-10], [t2,10]], attrGlid);
  var pX0 = brd0.create('glider', [t0,  x0, lX0], attrPmov() ),
      pX1 = brd0.create('glider', [t1,  x1, lX1], attrPmov() ),
      pX2 = brd0.create('glider', [t2,  x2, lX2], attrPmov() );
  brd0.create('polygonalchain', [ pX0, pX1, pX2 ], attrLine);
  brd0.unsuspendUpdate();


  // Q: Are these updates necessary?
  // brd0.update();

  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pX0.Y(); });
  question.bindInput(1, () => { return pX1.Y(); });
  question.bindInput(2, () => { return pX2.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID0, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Der erste Punkt liegt im Ursprung.</li>
<li>Für die Masse \( {m1}\,\mathrm{ g } \) beträgt die Gewichtskraft \( F_\mathrm{F} = m\cdot g = {=m1/1000}\,\mathrm{kg} \cdot {g}\,\mathrm{N/kg} = {F1}\,\mathrm{N} \).</li>
<li>Für die Masse \( {m2}\,\mathrm{ g } \) beträgt die Gewichtskraft \( F_\mathrm{F} = m\cdot g = {=m2/1000}\,\mathrm{kg} \cdot {g}\,\mathrm{N/kg} = {F2}\,\mathrm{N}  \).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8279  -->
  <question type="formulas">
    <name>
      <text>Gewicht eines Astronauten auf dem Mars</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Auf dem Mars ist die Fallbeschleunigung 2.5mal kleiner als auf der Erde. Auf dem Mond ist sie 6mal kleiner. Ein Astronaut, dessen Masse auf dem Mond {m} kg beträgt, betritt die Marsoberfläche.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m = {80:111};</text>
</varsrandom>
<varsglobal><text>FG = sigfig( m*9.81/2.5 , 2);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>FG</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>N: h;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie gross ist dort sein Gewicht ungefähr?
&emsp;&emsp; {_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8280  -->
  <question type="formulas">
    <name>
      <text>Masse eines Velos aus seinem Gewicht</text>
    </name>
    <questiontext format="markdown">
      <text>Ein Velo habe ein Gewicht von \( {FG}\,\mathrm{N} \).</text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>FG = {57:130:2};</text>
</varsrandom>
<varsglobal><text>g = 9.81;

m = FG / g;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = m;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
  divTest = round( shiftedNum - floor(shiftedNum) , 6);
  nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 1.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs(mantAns - mantSolSFA) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs(mantAns - mantSolSFA) < 25 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( 0.7*critMantCorrect, 0.6*critMantRnd1, 0.2*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>kg</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist seine Masse?</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Druck und Schweredruck</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8284  -->
  <question type="formulas">
    <name>
      <text>Änderung des Auflagedrucks mit Schneeschuhen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Sie spazieren mit Schneeschuhen, welche die <em>{strFactorA} Fläche</em> Ihrer Füsse besitzen, über eine schneebedeckte Winterlandschaft.</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>1.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>jA = {0:6};</text>
</varsrandom>
<varsglobal><text><![CDATA[strFactorA = ["doppelte","dreifache","vierfache","fünffache","sechsfache","siebenfache"][jA];

factorA = [2,3,4,5,6,7][jA];

factorP = 1 / factorA;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = factorP;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}
nSigFigAns = max( nSigFig, nSigFigAns);

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critExp > 0 ) ? 0.3*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor verändert sich durch die Verwendung der Schneeschuhe der mittlere Druck auf die Unterlage?</p>
<p>&emsp;{_0}&emsp; (Angabe mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8285  -->
  <question type="formulas">
    <name>
      <text>Schweredruck in einer Flüssigkeit</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Der blaue Graph im p(h)-Diagramm soll den hydrostatischen Druck in {strFluid} in Abhängigkeit der Eintauchtiefe h angeben. Verschieben Sie entsprechend den roten Punkt.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>j = {0:6};
h = {150:400};</text>
</varsrandom>
<varsglobal><text><![CDATA[strFluid = ["Aceton", "Benzol", "Benzin", "Diethylether", "Glycerin", "Olivenöl"][j];
rho = [0.788, 0.878, 0.744, 0.714, 1.261, 0.92][j];

pH = round(rho * 9.81 * h/100);]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>pH</text>
 </answer>
 <vars2>
  <text><![CDATA[crit_Correct = ( abs( _0 - pH ) < 0.5 );
crit_Rounding = ( abs( _0 - pH ) < 1.5 );

grade = max( crit_Correct, 0.8*crit_Rounding );]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="450" height="300" numberOfBoards="1" ext_formulas>
var jsxCode = function (question) {

	//-- Settings and general definitions

	const bDraft = true;
	const isMoodle = (typeof question !== 'undefined');
	// const element = document.getElementById("page");
	// const isMoodle = (typeof(element) != 'undefined' && element != null);

	const questionIsSolved = isMoodle ? question.isSolved : false;

	JXG.Options.elements.highlight = false;
	JXG.Options.point.snapToGrid = true;
	JXG.Options.point.snapSizeX = 1;
	JXG.Options.point.snapSizeY = 1;
	JXG.Options.point.showInfobox = true;
	JXG.Options.point.infoboxDigits = 0;
	JXG.Options.infobox.fontSize = 13;
	JXG.Options.infobox.strokeColor = '#000';
	JXG.Options.board.showCopyright= false;
	JXG.Options.board.showNavigation= false;
	JXG.Options.board.zoom= {enabled: false, wheel: false};
	JXG.Options.board.pan= {enabled: false, needTwoFingers: false};
	// JXG.Options.arrow.lastArrow.size = 4;
	// JXG.Options.arrow.lineCap = 'round';

	const colRed = '#F44336', colPink = '#E91E63', colPurple = '#9C27B0', colBlue = '#2196F3',
		colIndigo = '#3F51B5', colCyan = '#00BCD4', colGreen = '#4CAF50', colYellow = '#FFEB3B',
		colAmber = '#FFC107', colOrange = '#FF9800', colBrown = '#795548', colGrey = '#9E9E9E';


	//-- Set parameters for before and after submission

	var xP = isMoodle ? {h} : 270;
	var yP;
	[yP] = isMoodle ? question.getAllValues([0]) : [0];


	//-- Create boards

	const brds = question.initBoards([{
		boundingbox: [-50, 56, 490, -5]
	}]);
	const [brd0] = brds;

	if (bDraft) console.log(brd0);


	//-- Draw axes

	brd0.create('axis', [[0, 0], [450, 0]], {
		straightFirst: false, straightLast: false,
		withLabel: true, name: 'h (cm)',
		label: { position: 'rt', offset: [12, 15], anchorX: 'right', parse: true, fontSize: 12 },
		ticks: { drawZero: true, insertTicks: false, 
			ticksDistance: 100, majorHeight: () => 0 * 2 * brd0.unitX, majorTickEndings: [0, 0],
			minorTicks: 0, minorHeight: () => 0 * 2 * brd0.unitX, tickEndings: [0, 0],
			label: { visible: true, anchorX: 'middle', anchorY: 'top', offset: [0, -4] }
		}
	});
	brd0.create('axis', [[0, 0], [0, 53]], {
		straightFirst: false, straightLast: false,
		withLabel: true, name: 'p – p_S (kPa)',
		label: { position: 'rt', offset: [10, 3], anchorX: 'left', parse: true, fontSize: 12 },
		ticks: { drawZero: true, insertTicks: false, 
			ticksDistance: 10, majorHeight: () => 0 * 2 * brd0.unitX, majorTickEndings: [0, 0],
			minorTicks: 0, minorHeight: () => 0 * 2 * brd0.unitX, tickEndings: [0, 0],
			label: { visible: true, anchorX: 'right', anchorY: 'middle', offset: [-3, 0] }
		}
	});

	
	//-- Graphical elements on the boards

	function attrPmov(addAttr = {}) {
		const attr = { fixed: questionIsSolved, withLabel: false, color: colRed,
			strokeWidth:0, size:3};
		//   const attr = {fixed: question.isSolved, withLabel: false};
		return { ...attr, ...addAttr };
	}
	const attrLine = { borders: { strokeColor: colBlue, strokeWidth: 3 } };

	const lineX = brd0.create('segment', [[xP,0], [xP,50]], {visible: false} );
	var pP = brd0.create('glider', [xP, yP, lineX], attrPmov({  }));
	
	function f(x) {
		return pP.Y() / pP.X() * x;
	}
	brd0.create('functiongraph', [f, 0, 420], { strokeColor: colBlue, strokeWidth: 2, dash: 0 });


	//-- Send parameters to Formulas

	question.bindInput(0, () => { return pP.Y(); });

};
new JSXQuestion(BOARDIDS, jsxCode, allowInputEntry=false);
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="html">
<text>Rechnung:
\[ p-p_\mathrm{S} = \rho g h
= {rho}\cdot 10^3\,\mathrm{kg/m^3}\cdot 9.81\,\mathrm{m/s^2}\cdot{=h/100}\,\mathrm{m}
= {pH}\,\mathrm{kPa} \]</text>
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</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Auftrieb</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8297  -->
  <question type="formulas">
    <name>
      <text>Boot fällt vom Kran ins Wasser</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Zur Überwinterung wird ein Motorboot von \( {mBoot}\,\mathrm{t} \) mit Hilfe eines Krans aus dem See geholt, siehe Abbildung.<p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/fig_BootAmKran.jpg" width="250px"/></p><br>

<p>Geben Sie die Ergebniss zu den folgenden Fragen als Zahlenwert zur jeweils vorgegebenen Einheit an. Sie können die Dichte von Wasser als \( 1.0\,\mathrm{t/m^3} \) annehmen. <em>Tipp: Vorzeichen beachten!</em></p>]]></text>
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</file>
    </questiontext>
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      <text></text>
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      <text>Die Antwort ist richtig.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Die Antwort ist teilweise richtig.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Die Antwort ist falsch.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>mBoot = {1.3:3.5:0.2};
rhoBoot = {1.1:2.1:0.2};</text>
</varsrandom>
<varsglobal><text><![CDATA[rhoFl = 1.0;

DeltaV1 = - mBoot / rhoFl;
DeltaV2 = mBoot / rhoBoot; 

strWasserstand = ["Der Wasserstand sinkt.", "Der Wasserstand bleibt gleich.", "Der Wasserstand steigt."];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = DeltaV1;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 0.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( abs(mantAns) - abs(mantSolSFA) ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );

# Correct sign (positive/negative)
critSign = ( origAns * origSol > 0 );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.3*critSign + 0.4*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie verändert sich beim Herausheben des Bootes der Wasserstand des Sees? [40%]</p>
<p>&emsp;{_0}&nbsp;\( \mathrm{m^3} \)&emsp; (Angabe mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = DeltaV2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)));
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs(expAns-expSol) < 0.5 );

# Correct number of significant figures
critSigFig = ( abs(nSigFigAns-nSigFig) < 0.5 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( abs(mantAns) - abs(mantSolSFA) ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs(mantAns - mantSolSFA) < 1.5 * 0.1**(nSigFigAns-1) );

# Correct sign (positive/negative)
critSign = ( origAns * origSol > 0 );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.3*critSign + 0.4*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Während das Boot noch in der Luft ist, reissen die Riemen und das Boot fällt so unglücklich ins Wasser, dass es vollläuft und sinkt. Wie verändert sich dabei der Wasserstand des Sees, wenn die mittlere Dichte des Bootes \( {rhoBoot}\,\mathrm{t/m^3} \) beträgt? [40%]</p>
<p>&emsp;{_0}&nbsp;\( \mathrm{m^3} \)&emsp; (Angabe mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>0</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie verändert sich bei dem gesamten Vorgang der Wasserstand des Sees? [20%]</p>
<p>&emsp;{_0:strWasserstand:MCE}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Gleichförmig geradlinige Bewegung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8357  -->
  <question type="formulas">
    <name>
      <text>Diagramme: x(t) aus v_x(t)-Daten</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper startet an der Position \({x0}\,\mathrm{m}\) auf der \(x\)-Achse. Dann bewegt er sich 
<ul>
<li>für \({Dt1}\,\mathrm{s}\) mit \({v1}\,\mathrm{m/s}\),</li>
<li>für \({Dt2}\,\mathrm{s}\) mit \({v2}\,\mathrm{m/s}\) und</li>
<li>für \({Dt3}\,\mathrm{s}\) mit \({v3}\,\mathrm{m/s}\).</li>
</ul>
</p>
<p>Stellen Sie die Bewegung im \(x(t)\)-Diagramm richtig dar.</p>
<br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
vArray = shuffle([-3:3.1:0.1]);
x0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; x0 = round( x0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
xMax = 10; xMin = -10;

v1 = round( vArray[0] , 1);
for (j:[1:7]) { v1 = ( x0+v1*Dt1 > xMax) ? (v1 - 0.5) : ( ( x0+v1*Dt1 < xMin) ? (v1 + 0.5) : v1 ); }
Dx1 = round( v1*Dt1 , 1);
x1 = x0 + Dx1;

v2= round( vArray[1] , 1);
for (j:[1:7]) { v2 = ( x1+v2*Dt2 > xMax) ? (v2 - 0.5) : ( ( x1+v2*Dt2 < xMin) ? (v2 + 0.5) : v2 ); }
Dx2 = round( v2*Dt2 , 1);
x2 = x1 + Dx2;

v3 = round( vArray[2] , 1);
for (j:[1:7]) { v3 = ( x2+v3*Dt3 > xMax) ? (v3 - 0.5) : ( ( x2+v3*Dt3 < xMin) ? (v3 + 0.5) : v3 ); }
Dx3 = round( v3*Dt3 , 1);
x3 = x2 + Dx3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>7</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , x0,x1,x2,x3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_x0 = _3;
S_x1 = _4;
S_x2 = _5;
S_x3 = _6;

S_Dx1 = _4-_3;
S_Dx2 = _5-_4;
S_Dx3 = _6-_5;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );
crit_x0 = ( abs( S_x0 - x0 ) < 0.01 );

crit_Dx1 = ( abs( S_Dx1 - Dx1) <0.01 );
crit_Dx2 = ( abs( S_Dx2 - Dx2) <0.01 );
crit_Dx3 = ( abs( S_Dx3 - Dx3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + crit_x0 + 2*crit_Dx1 + 2*crit_Dx2 + 2*crit_Dx3 ) / 10;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="400" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
JXG.Options.elements.highlight = false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

JXG.Options.point.infoboxDigits = 1;
JXG.Options.point.snapSizeX = 1;
JXG.Options.point.snapSizeY = 0.1;

  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color: col1, size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false, color: col2};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  var t1,t2,t3 , x0,x1,x2,x3;
  [t1,t2,t3 , x0,x1,x2,x3] = 
    question.getAllValues([1,2,3 , 1,1,1,1 ]);

  
  // Create boards
  var brd0 = question.initBoard(BOARDID0, { 
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
    });
    

  // Define lines and points on brd0
  // Q: Is it necessary/beneficial/wrong to suspendUpdate here?
  brd0.suspendUpdate();
  var lX0 = brd0.create('line', [[0,-10], [0,10]], attrGlid);
  var pX0 = brd0.create('glider', [0,  x0, lX0], attrPmov() ),
      pX1 = brd0.create('point',  [t1, x1], attrPmov() ),
      pX2 = brd0.create('point',  [t2, x2], attrPmov() ),
      pX3 = brd0.create('point',  [t3, x3], attrPmov() );
  brd0.create('polygonalchain', [ pX0, pX1, pX2, pX3 ], attrLine);
  brd0.unsuspendUpdate();


  // Q: Are these updates necessary?
  // brd0.update();

  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pX1.X(); });
  question.bindInput(1, () => { return pX2.X(); });
  question.bindInput(2, () => { return pX3.X(); });
  question.bindInput(3, () => { return pX0.Y(); });
  question.bindInput(4, () => { return pX1.Y(); });
  question.bindInput(5, () => { return pX2.Y(); });
  question.bindInput(6, () => { return pX3.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID0, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Im Zeitintervall \( \left[{t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \) bewegt sich der Körper von \( {x0}\,\mathrm{m} \) nach \( {x1}\,\mathrm{m} \) (Verschiebung \( {Dx1}\,\mathrm{m} \)).</li>
<li>Im Zeitintervall \( \left[{t1}\,\mathrm{s}; {t2}\,\mathrm{s} \right] \) bewegt sich der Körper von \( {x1}\,\mathrm{m} \) nach \( {x2}\,\mathrm{m} \) (Verschiebung \( {Dx2}\,\mathrm{m} \)).</li>
<li>Im Zeitintervall \( \left[{t2}\,\mathrm{s}; {t3}\,\mathrm{s} \right] \) bewegt sich der Körper von \( {x2}\,\mathrm{m} \) nach \( {x3}\,\mathrm{m} \) (Verschiebung \( {Dx3}\,\mathrm{m} \)).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8358  -->
  <question type="formulas">
    <name>
      <text>Diagramme: x(t) aus v_x(t)-Diagramm</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper startet an der Position \({x0}\,\mathrm{m}\) auf der \(x\)-Achse. Dann bewegt er sich gemäss des dargestellten \(v_x(t)\)-Graphs.</p>
<p>Stellen Sie die Bewegung im \(x(t)\)-Diagramm richtig dar.</p>
<p>(Tipp: Beim Hovern über den Punkten im \(v_x(t)\)-Diagramm werden deren Koordinaten angezeigt.)</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
vArray = shuffle([-3:3.1:0.1]);
x0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; x0 = round( x0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
xMax = 10; xMin = -10;

v1 = round( vArray[0] , 1);
for (j:[1:7]) { v1 = ( x0+v1*Dt1 > xMax) ? (v1 - 0.5) : ( ( x0+v1*Dt1 < xMin) ? (v1 + 0.5) : v1 ); }
Dx1 = round( v1*Dt1 , 1);
x1 = x0 + Dx1;

v2= round( vArray[1] , 1);
for (j:[1:7]) { v2 = ( x1+v2*Dt2 > xMax) ? (v2 - 0.5) : ( ( x1+v2*Dt2 < xMin) ? (v2 + 0.5) : v2 ); }
Dx2 = round( v2*Dt2 , 1);
x2 = x1 + Dx2;

v3 = round( vArray[2] , 1);
for (j:[1:7]) { v3 = ( x2+v3*Dt3 > xMax) ? (v3 - 0.5) : ( ( x2+v3*Dt3 < xMin) ? (v3 + 0.5) : v3 ); }
Dx3 = round( v3*Dt3 , 1);
x3 = x2 + Dx3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>4</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>7</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , x0,x1,x2,x3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_x0 = _3;
S_x1 = _4;
S_x2 = _5;
S_x3 = _6;

S_Dx1 = _4-_3;
S_Dx2 = _5-_4;
S_Dx3 = _6-_5;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );
crit_x0 = ( abs( S_x0 - x0 ) < 0.01 );

crit_Dx1 = ( abs( S_Dx1 - Dx1) <0.01 );
crit_Dx2 = ( abs( S_Dx2 - Dx2) <0.01 );
crit_Dx3 = ( abs( S_Dx3 - Dx3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + crit_x0 + 2*crit_Dx1 + 2*crit_Dx2 + 2*crit_Dx3 ) / 10;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="400" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
JXG.Options.elements.highlight = false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

JXG.Options.point.infoboxDigits = 1;
JXG.Options.point.snapSizeX = 1;
JXG.Options.point.snapSizeY = 0.1;
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  var t1,t2,t3 , x0,x1,x2,x3;
  [t1,t2,t3 , x0,x1,x2,x3] = 
    question.getAllValues([1,2,3 , 1,1,1,1 ]);

  
  // Create boards
  var brd0 = question.initBoard(BOARDID0, { 
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
    });
    
  var brd1 = JXG.JSXGraph.initBoard(BOARDID1, { 
    boundingbox: [-1, 3.5, 12.5, -3.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
    });
      
  
  // Define lines and points on brd1
  brd1.suspendUpdate();
  var pV1  = brd1.create('point', [{t1}, {v1}], attrPfix() ),
      pV2  = brd1.create('point', [{t2}, {v2}], attrPfix() ),
      pV3  = brd1.create('point', [{t3}, {v3}], attrPfix() ),
      pV01 = brd1.create('point', [0,    {v1}], attrPfix() ),
      pV12 = brd1.create('point', [{t1}, {v2}], attrPfix() ),
      pV23 = brd1.create('point', [{t2}, {v3}], attrPfix() );
  brd1.create('polygonalchain', [ pV01, pV1, pV12, pV2, pV23, pV3 ], attrLine);
  brd1.unsuspendUpdate();


  // Define lines and points on brd0
  // Q: Is it necessary/beneficial/wrong to suspendUpdate here?
  brd0.suspendUpdate();
  var lX0 = brd0.create('line', [[0,-10], [0,10]], attrGlid);
  var pX0 = brd0.create('glider', [0,  x0, lX0], attrPmov() ),
      pX1 = brd0.create('point',  [t1, x1], attrPmov() ),
      pX2 = brd0.create('point',  [t2, x2], attrPmov() ),
      pX3 = brd0.create('point',  [t3, x3], attrPmov() );
  brd0.create('polygonalchain', [ pX0, pX1, pX2, pX3 ], attrLine);
  brd0.unsuspendUpdate();


  // Q: Are these updates necessary?
  // brd0.update();
  // brd1.update();

  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pX1.X(); });
  question.bindInput(1, () => { return pX2.X(); });
  question.bindInput(2, () => { return pX3.X(); });
  question.bindInput(3, () => { return pX0.Y(); });
  question.bindInput(4, () => { return pX1.Y(); });
  question.bindInput(5, () => { return pX2.Y(); });
  question.bindInput(6, () => { return pX3.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID0, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Im Zeitintervall \( \left[{t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \) bewegt sich der Körper von \( {x0}\,\mathrm{m} \) nach \( {x1}\,\mathrm{m} \) (Verschiebung \( {Dx1}\,\mathrm{m} \)).</li>
<li>Im Zeitintervall \( \left[{t1}\,\mathrm{s}; {t2}\,\mathrm{s} \right] \) bewegt sich der Körper von \( {x1}\,\mathrm{m} \) nach \( {x2}\,\mathrm{m} \) (Verschiebung \( {Dx2}\,\mathrm{m} \)).</li>
<li>Im Zeitintervall \( \left[{t2}\,\mathrm{s}; {t3}\,\mathrm{s} \right] \) bewegt sich der Körper von \( {x2}\,\mathrm{m} \) nach \( {x3}\,\mathrm{m} \) (Verschiebung \( {Dx3}\,\mathrm{m} \)).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8359  -->
  <question type="formulas">
    <name>
      <text>Diagramme: x(t) und v_x(t) aus v_x(t)-Daten</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper startet an der Position \({x0}\,\mathrm{m}\) auf der \(x\)-Achse. Dann bewegt er sich 
<ul>
<li>für \({Dt1}\,\mathrm{s}\) mit \({v1}\,\mathrm{m/s}\),</li>
<li>für \({Dt2}\,\mathrm{s}\) mit \({v2}\,\mathrm{m/s}\) und</li>
<li>für \({Dt3}\,\mathrm{s}\) mit \({v3}\,\mathrm{m/s}\).</li>
</ul>
</p>
<p>Stellen Sie die Bewegung in beiden Diagrammen richtig dar. Am besten gehen Sie so vor:
<ol>
<li>Stellen Sie im \(v_x(t)\)-Diagramm die Zeitspannen richtig ein, indem Sie die roten Punkte horizontal verschieben.</li>
<li>Verschieben Sie die roten Punkte im \(v_x(t)\)-Diagramm entsprechend der angegebenen Geschwindigkeiten.</li>
<li>Platzieren Sie die roten Punkte im \(x(t)\)-Diagramm gemäss der resultierenden Verschiebungen.</li>
</ol></p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
vArray = shuffle([-3:3.1:0.1]);
x0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; x0 = round( x0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
xMax = 10; xMin = -10;

v1 = round( vArray[0] , 1);
for (j:[1:7]) { v1 = ( x0+v1*Dt1 > xMax) ? (v1 - 0.5) : ( ( x0+v1*Dt1 < xMin) ? (v1 + 0.5) : v1 ); }
Dx1 = round( v1*Dt1 , 1);
x1 = x0 + Dx1;

v2= round( vArray[1] , 1);
for (j:[1:7]) { v2 = ( x1+v2*Dt2 > xMax) ? (v2 - 0.5) : ( ( x1+v2*Dt2 < xMin) ? (v2 + 0.5) : v2 ); }
Dx2 = round( v2*Dt2 , 1);
x2 = x1 + Dx2;

v3 = round( vArray[2] , 1);
for (j:[1:7]) { v3 = ( x2+v3*Dt3 > xMax) ? (v3 - 0.5) : ( ( x2+v3*Dt3 < xMin) ? (v3 + 0.5) : v3 ); }
Dx3 = round( v3*Dt3 , 1);
x3 = x2 + Dx3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>4</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>9</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , v1,v2,v3 , x1,x2,x3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_v1 = _3;
S_v2 = _4;
S_v3 = _5;
S_x1 = _6;
S_x2 = _7;
S_x3 = _8;

S_Dx1 = S_x1-x0;
S_Dx2 = S_x2-S_x1;
S_Dx3 = S_x3-S_x2;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );

crit_v1 = ( abs( S_v1 - v1 ) < 0.01 );
crit_v2 = ( abs( S_v2 - v2 ) < 0.01 );
crit_v3 = ( abs( S_v3 - v3 ) < 0.01 );

crit_Dx1 = ( abs( S_Dx1 - Dx1) <0.01 );
crit_Dx2 = ( abs( S_Dx2 - Dx2) <0.01 );
crit_Dx3 = ( abs( S_Dx3 - Dx3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + 2*crit_Dx1 + 2*crit_Dx2 + 2*crit_Dx3 + crit_v1 + crit_v2 + crit_v3) / 12;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="300" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
// Import final coordinates after submission
var x0={x0};
var t1,t2,t3 , v1,v2,v3 , x1,x2,x3;
[t1,t2,t3 , v1,v2,v3 , x1,x2,x3] = 
question.getAllValues([1,2,3 , 1,2,3 , x0,x0,x0 ]);

JXG.Options.elements.highlight = false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

JXG.Options.point.infoboxDigits = 1;
JXG.Options.point.snapSizeX = 1;
JXG.Options.point.snapSizeY = 0.1;

const colRed = '#F44336', colPink = '#E91E63', colPurple = '#9C27B0', colBlue = '#2196F3',
      colIndigo = '#3F51B5', colCyan = '#00BCD4', colGreen = '#4CAF50', colYellow = '#FFEB3B',
      colAmber = '#FFC107', colOrange = '#FF9800', colBrown = '#795548', colGrey = '#9E9E9E';


// Create boards
var brds = question.initBoards( [
{ // attribs for BOARDID0 
  boundingbox: [-1, 12, 13, -11], axis:true,
  defaultAxes: {
    x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
        label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
    y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
        label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
  },
  { // attribs for BOARDID1 
  boundingbox: [-1, 3.8, 12, -3.5], axis:true,
  defaultAxes: {
    x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
        label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
    y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
        label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
  }
] );

var brd0 = brds[0];
var brd1 = brds[1];
// console.log(brd0, brd1);

// Board brd0 needs to be updated when changes in brd1 occur
// question.addChildsAsc();


// Attributes for points and lines
function attrPfix(addAttr={}) {
  const attr = {fixed: true, visible: false, withLabel: false};
  return { ...attr, ...addAttr}; 
}
function attrPmov(addAttr={}) {
  const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false, color: colRed};
  return { ...attr, ...addAttr};
}
function attrPsma(addAttr={}) {
  const attr = {visible: true, withLabel: false, color: colBlue, size: 1};
  return { ...attr, ...addAttr};
}
const attrLine = {borders: {strokeColor: colBlue, strokeWidth: 3} };
const attrGlid = {visible:false};


// Define lines and points on brd1
var pV1 = brd1.create('point',  [t1, v1], attrPmov({name: "pV1"}) ),
    pV2 = brd1.create('point',  [t2, v2], attrPmov({name: "pV2"}) ),
    pV3 = brd1.create('point',  [t3, v3], attrPmov({name: "pV3"}) ),
    pV01 = brd1.create('point', [0, "Y(pV1)"], attrPsma() ),
    pV12 = brd1.create('point', ["X(pV1)", "Y(pV2)"], attrPsma() ),
    pV23 = brd1.create('point', ["X(pV2)", "Y(pV3)"], attrPsma() )    ;
    pV34 = brd1.create('point', ["X(pV3)", 0], attrPsma() )    ;
brd1.create('polygonalchain', [ pV01, pV1, pV12, pV2, pV23, pV3, pV34 ], attrLine);


// Define lines and points on brd0
var pX0 = brd0.create('point', [0, x0], attrPsma({fixed: true}) ),
    pX1 = brd0.create('point', [t1, x1], attrPmov({face: 'diamond'}) ),
    pX2 = brd0.create('point', [t2, x2], attrPmov({face: 'diamond'}) ),
    pX3 = brd0.create('point', [t3, x3], attrPmov({face: 'diamond'}) );
brd0.create('polygonalchain', [ pX0, pX1, pX2, pX3 ], attrLine);


// Define dependencies
pV1.on('drag', function() { pX1.moveTo([this.X(), pX1.Y()], 0); });
pV2.on('drag', function() { pX2.moveTo([this.X(), pX2.Y()], 0); });
pV3.on('drag', function() { pX3.moveTo([this.X(), pX3.Y()], 0); });
// pX1.on('drag', function() { pV1.moveTo([this.X(), pV1.Y()], 0); });
// pX2.on('drag', function() { pV2.moveTo([this.X(), pV2.Y()], 0); });
// pX3.on('drag', function() { pV3.moveTo([this.X(), pV3.Y()], 0); });
pX1.on('drag', function() { this.moveTo([pV1.X(), this.Y()], 0); });
pX2.on('drag', function() { this.moveTo([pV2.X(), this.Y()], 0); });
pX3.on('drag', function() { this.moveTo([pV3.X(), this.Y()], 0); });


// Whenever the construction is altered the values of the points are sent to formulas.
question.bindInput(0, () => { return pV1.X(); });
question.bindInput(1, () => { return pV2.X(); });
question.bindInput(2, () => { return pV3.X(); });
question.bindInput(3, () => { return pV1.Y(); });
question.bindInput(4, () => { return pV2.Y(); });
question.bindInput(5, () => { return pV3.Y(); });
question.bindInput(6, () => { return pX1.Y(); });
question.bindInput(7, () => { return pX2.Y(); });
question.bindInput(8, () => { return pX3.Y(); });
};

// Execute the JavaScript code.
new JSXQuestion(BOARDIDS, jsxCode, allowInputEntry=false); // use BOARDIDS here!!
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8360  -->
  <question type="formulas">
    <name>
      <text>Ian und Eric schwimmen auf der 50m Bahn</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ian Thorpe und Eric Moussambani schwimmen auf der 50 m Bahn um die Wette. Für 100 m benötigt Ian {tA2} s und Eric {tB2} s.</p>
<p>Nehmen Sie konstante Schnelligkeit an und vernachlässigen Sie die Zeit fürs Wenden.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>tA2 = {48:60:2};
tB2 = {112:126:2};</text>
</varsrandom>
<varsglobal><text>tA1 = tA2/2;
tB1 = tB2/2;

xA1 = 50;
xA2 = 0;
xB1 = 50;
xB2 = 0;

tP = (tB1 * (tA2 * xA1 - tA1 * xA2)) / (tB1 * xA1 - tB1 * xA2 - tA1 * xB1 + tA2 * xB1);
tP = round(tP);
xP = ((tA2 * xA1 - tA1 * xA2) * xB1) / (tB1 * xA1 - tB1 * xA2 - tA1 * xB1 + tA2 * xB1);
xP = round(xP);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2.8</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>8</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[ tA1,xA1 , tA2,xA2 , tB1,xB1 , tB2,xB2 ]</text>
 </answer>
 <vars2>
  <text>critA1 = ( (_0==tA1) + (_1==xA1) ) / 2;
critA2 = ( (_2==tA2) + (_3==xA2) ) / 2;
critB1 = ( (_4==tB1) + (_5==xB1) ) / 2;
critB2 = ( (_6==tB2) + (_7==xB2) ) / 2;

criterion = ( critA1 + critA2 + critB1 + critB2 ) / 4;</text>
 </vars2>
 <correctness>
  <text>criterion</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Verschieben Sie die roten Punkte so, dass die Graphen die Bewegung der beiden Schwimmer repräsentieren. [2.8 P]</p>

<jsxgraph width="600" height="400" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {

    JXG.Options.elements.highlight = false;
    JXG.Options.point.snapToGrid = true;
    JXG.Options.point.snapSizeX = 1;
    JXG.Options.point.snapSizeY = 1;
    JXG.Options.point.showInfobox = true;
    JXG.Options.point.infoboxDigits = 0;
    JXG.Options.infobox.fontSize = 13;
    JXG.Options.infobox.strokeColor = '#000';
    JXG.Options.board.showCopyright= false;
    JXG.Options.board.showNavigation= false;
    JXG.Options.board.zoom= {enabled: false, wheel: false};
    JXG.Options.board.pan= {enabled: false, needTwoFingers: false};
    // JXG.Options.arrow.lastArrow.size = 4;
    // JXG.Options.arrow.lineCap = 'round';

    const colRed = '#F44336', colPink = '#E91E63', colPurple = '#9C27B0', colBlue = '#2196F3',
        colIndigo = '#3F51B5', colCyan = '#00BCD4', colGreen = '#4CAF50', colYellow = '#FFEB3B',
        colAmber = '#FFC107', colOrange = '#FF9800', colBrown = '#795548', colGrey = '#9E9E9E';

    // Import final coordinates after submission
    var tA1, xA1, tA2, xA2, tB1, xB1, tB2, xB2;
    [tA1, xA1, tA2, xA2, tB1, xB1, tB2, xB2] = 
        question.getAllValues([4,25, 10,35, 10,20, 20,30 ]);

    // Initialize the construction
    var brd = question.initBoard({
        boundingbox: [-10, 55, 130, -5], axis:true,
        defaultAxes: {
            x: {withLabel: true, name: 't in s',
                label: {position: 'rt', offset: [-0, 15], anchorX: 'right'} },
            y: {withLabel:true, name: 'x in m',      
                label: {position: 'rt', offset: [+15, -0]} } },
            showCopyright: false, showNavigation: false,
            zoom: {enabled:false, wheel: false},
            pan: {enabled:false, needTwoFingers: false}
        });

    // The four points fixated to the lines, called 'gliders'.
    var pA = [];
    pA.push(brd.create('point', [0, 0],
        {fixed: true, visible: false, withLabel: false} ));
    pA.push(brd.create('point', [tA1, xA1],
        {fixed: question.isSolved, color: colRed, snapToGrid: true, withLabel: false} ));
    pA.push(brd.create('point', [tA2, xA2],
        {fixed: question.isSolved, color: colRed, snapToGrid: true, name: 'Ian',
         label:{color: colBlue, fontSize: 16} } ));
    var pB = [];
    pB.push(brd.create('point', [0, 0],
        {fixed: true, visible: false, withLabel: false} ));
    pB.push(brd.create('point', [tB1, xB1],
        {fixed: question.isSolved, color: colRed, snapToGrid: true, withLabel: false} ));
    pB.push(brd.create('point', [tB2, xB2],
        {fixed: question.isSolved, color: colRed, snapToGrid: true, name: 'Eric',
         label:{color: colGreen, fontSize: 16} } ));

    // Polygonal chain, aka. polyline, through the points
    brd.create('polygonalchain', pA,
        {borders: {strokeColor: colBlue, strokeWidth: 3} });
     brd.create('polygonalchain', pB,
         {borders: {strokeColor: colGreen, strokeWidth: 3} });

    brd.update();

    // Whenever the construction is altered the values of the points are sent to formulas.
    question.bindInput(0, () => { return pA[1].X(); });
    question.bindInput(1, () => { return pA[1].Y(); });
    question.bindInput(2, () => { return pA[2].X(); });
    question.bindInput(3, () => { return pA[2].Y(); });
    question.bindInput(4, () => { return pB[1].X(); });
    question.bindInput(5, () => { return pB[1].Y(); });
    question.bindInput(6, () => { return pB[2].X(); });
    question.bindInput(7, () => { return pB[2].Y(); });
};

// Execute the JavaScript code.
new JSXQuestion(BOARDID, jsxCode, allowInputEntry=false);

</jsxgraph>]]></text>
 </subqtext>
 <feedback format="html">
<text><![CDATA[<p>Die Punkte auf Ians Graph sollten sein: ({tA1} s; {xA1} m) und ({tA2} s; {xA2} m)</p>
<p>Die Punkte auf Erics Graph sollten sein: ({tB1} s; {xB1} m) und ({tB2} s; {xB2} m)</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>0.6</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>tP</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 1.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie lange nach dem Start begegnen sich Ian und Eric? [0.6 P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>0.6</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>xP</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 1.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>In welcher Entfernung vom Startblock begegnen sich Ian und Eric? [0.6 P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Gleichmässig beschleunigte Bewegung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8367  -->
  <question type="formulas">
    <name>
      <text>Bahnparameter aus x(t)-Graph ablesen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Wählen Sie die richtigen Vergleichsoperatoren für die Bahnparameter der dargestellten Kurven im \(x(t)\)-Diagramm aus.</p>

<p><jsxgraph width="400" height="300">

  // Attributes for points and lines
  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';

  var brd = JXG.JSXGraph.initBoard(BOARDID, {
    boundingbox: [-1, 7, 11, -7], axis:true,
    defaultAxes: {
      x: { ticks: {visible: false}, withLabel: true, name: '$$t$$',
          label: {position: 'rt', offset: [5, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: { ticks: {visible: false}, withLabel:true, name: '$$x$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
    zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright: false, showNavigation: false 
    });
  console.log(brd);

  // Define elements on brd0
  brd.create('functiongraph', [function(t){ return {sXB}*{nXB} + {sVB}*{nVB}*t + 0.5*{sAB}*{nAB}*t*t }, 0, 10], 
               {strokeColor:col1, strokeWidth:2 });
  brd.create('functiongraph', [function(t){ return {sXR}*{nXR} + {sVR}*{nVR}*t + 0.5*{sAR}*{nAR}*t*t }, 0, 10], 
               {strokeColor:col2, strokeWidth:2 });
  brd.create('functiongraph', [function(t){ return {sXG}*{nXG} + {sVG}*{nVG}*t + 0.5*{sAG}*{nAG}*t*t }, 0, 10], 
               {strokeColor:col3, strokeWidth:2 });

  // brd.update();
</jsxgraph><br></p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text># Blaue Kurve
jXB = {0,1,2};    nXB = {0.3:4:0.3};
jVB = {0,1,2};    nVB = {0.3:1.5:0.1};
jAB = {0,1,2};    nAB = {0.2:1:0.1};

# Rote Kurve
jXR = {0,1,2};    nXR = {0.3:4:0.3};
jVR = {0,1,2};    nVR = {0.3:1.5:0.1};
jAR = {0,1,2};    nAR = {0.2:1:0.1};

# Grüne Kurve
jXG = {0,1,2};    nXG = {0.3:4:0.3};
jVG = {0,1,2};    nVG = {0.3:1.5:0.1};
jAG = {0,1,2};    nAG = {0.2:1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[signs = [-1,0,1];
operators = ["< 0", "= 0", "> 0"];

# Blaue Kurve
sXB = signs[jXB];    oXB = operators[jXB];
sVB = signs[jVB];    oVB = operators[jVB];
sAB = signs[jAB];    oAB = operators[jAB];

# Rote Kurve
sXR = signs[jXR];    oXR = operators[jXR];
sVR = signs[jVR];    oVR = operators[jVR];
sAR = signs[jAR];    oAR = operators[jAR];

# Grüne Kurve
sXG = signs[jXG];    oXG = operators[jXG];
sVG = signs[jVG];    oVG = operators[jVG];
sAG = signs[jAG];    oAG = operators[jAG];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>3</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[jXB, jVB, jAB]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_jX = _0;
S_jV = _1;
S_jA = _2;

crit_jX = ( abs( S_jX - jXB ) < 0.01 );
crit_jV = ( abs( S_jV - jVB ) < 0.01 );
crit_jA = ( abs( S_jA - jAB ) < 0.01 );

crit_tot = ( crit_jX + crit_jV + crit_jA ) / 3;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Bahnparameter der <u>blauen</u> Kurve:</p>
<p>&emsp;&emsp;
\(x_0\;\;\){_0:operators:MCE}&emsp;&emsp;&emsp;&emsp;
\(v_{x,0}\;\;\){_1:operators:MCE}&emsp;&emsp;&emsp;&emsp;
\(a_x\;\;\){_2:operators:MCE}
</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>3</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[jXR, jVR, jAR]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_jX = _0;
S_jV = _1;
S_jA = _2;

crit_jX = ( abs( S_jX - jXR ) < 0.01 );
crit_jV = ( abs( S_jV - jVR ) < 0.01 );
crit_jA = ( abs( S_jA - jAR ) < 0.01 );

crit_tot = ( crit_jX + crit_jV + crit_jA ) / 3;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Bahnparameter der <u>roten</u> Kurve:</p>
<p>&emsp;&emsp;
\(x_0\;\;\){_0:operators:MCE}&emsp;&emsp;&emsp;&emsp;
\(v_{x,0}\;\;\){_1:operators:MCE}&emsp;&emsp;&emsp;&emsp;
\(a_x\;\;\){_2:operators:MCE}
</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>3</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[jXG, jVG, jAG]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_jX = _0;
S_jV = _1;
S_jA = _2;

crit_jX = ( abs( S_jX - jXG ) < 0.01 );
crit_jV = ( abs( S_jV - jVG ) < 0.01 );
crit_jA = ( abs( S_jA - jAG ) < 0.01 );

crit_tot = ( crit_jX + crit_jV + crit_jA ) / 3;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Bahnparameter der <u>grünen</u> Kurve:</p>
<p>&emsp;&emsp;
\(x_0\;\;\){_0:operators:MCE}&emsp;&emsp;&emsp;&emsp;
\(v_{x,0}\;\;\){_1:operators:MCE}&emsp;&emsp;&emsp;&emsp;
\(a_x\;\;\){_2:operators:MCE}
</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8368  -->
  <question type="formulas">
    <name>
      <text>Bestimmung von Bahngrössen aus den Diagrammen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Die {Premise} eines sich entlang der \(x\)-Achse bewegenden Objektes kann bestimmt werden aus</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>1.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>j = {0:4};</text>
</varsrandom>
<varsglobal><text><![CDATA[Premises = [
	"Geschwindigkeit",
	"Beschleunigung",
	"Verschiebung",
	"Geschwindigkeitsänderung"];

Conclusions = [
	"  der Steigung des \\(x(t)\\)-Graphen.",
	"  der Steigung des \\(v_x(t)\\)-Graphen.",
	"  der Fläche unter dem \\(v_x(t)\\)-Graphen.",
	"  der Fläche unter dem \\(a_x(t)\\)-Graphen."];

Premise = Premises[j];

ansJ = j ;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>ansJ</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>{_0:Conclusions}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>Konzept</text>
</tag>
      <tag><text>1P</text>
</tag>
    </tags>
  </question>

<!-- question: 8369  -->
  <question type="formulas">
    <name>
      <text>Bewegungsparabel nachzeichnen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Die blaue Kurve im \(x(t)\)-Diagramm markiert die Bewegung eines Körpers.</p>

<p>Passen Sie die Parameter \(x_0\), \(v_{x,0}\) und \(a_x\) so an, dass die rote Kurve genau die blaue Kurve nachzeichnet.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>x0 = {-3:3.1:0.1};
v0 = {-1.5:1.6:0.1};
a0 = {-1.5:1.6:0.1};</text>
</varsrandom>
<varsglobal><text></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>3</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[x0, v0, a0]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_x0 = _0;
S_v0 = _1;
S_a0 = _2;

crit_x0 = ( abs( S_x0 - x0 ) < 0.01 );
crit_v0 = ( abs( S_v0 - v0 ) < 0.01 );
crit_a0 = ( abs( S_a0 - a0 ) < 0.01 );

crit_tot = ( crit_x0 + crit_v0 + crit_a0 ) / 3;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400,400" height="400,100" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  // Import final coordinates after submission
  const x0fix = {x0}, v0fix = {v0}, a0fix = {a0};
  var x0, v0, a0;
  [ x0, v0, a0 ] = question.getAllValues([ 0, 0, 0 ]);
  
  // Attributes for points and lines
  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';


  // Create boards
  var brds = question.initBoards( [
  { // attribs for BOARDID0 
    boundingbox: [-1, 7, 11, -7], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
    zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright: false, showNavigation: false 
    },
    { // attribs for BOARDID1 
    boundingbox: [-4.2, 3.7, 12, 0.3], axis:false,
    zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright: false, showNavigation: false
    }
  ] );

  var brd0 = brds[0];
  var brd1 = brds[1];
  console.log(brd0);

  // Board brd0 needs to be updated when changes in brd1 occur
  // question.addChildsAsc();
  brd1.addChild(brd0);


  // Define elements on brd1
  var sliderX = brd1.create('slider',[[0,3],[10,3],[-3,x0,3]], {snapWidth: 0.1, precision:1 } ),
      sliderV = brd1.create('slider',[[0,2],[10,2],[-1.5,v0,1.5]], {snapWidth: 0.1, precision:1 } ),
      sliderA = brd1.create('slider',[[0,1],[10,1],[-1.5,a0,1.5]], {snapWidth: 0.1, precision:1 } );
  brd1.create('text',[-3.5, 3.4, '$$x_0\\;\\;\\mathrm{(m)}$$'      ], {fixed:true, parse:false, fontSize: 14 });
  brd1.create('text',[-3.5, 2.4, '$$v_{x,0}\\;\\;\\mathrm{(m/s)}$$'], {fixed:true, parse:false, fontSize: 14 });
  brd1.create('text',[-3.5, 1.4, '$$a_x\\;\\;\\mathrm{(m/s^2)}$$'  ], {fixed:true, parse:false, fontSize: 14 });


  // Define elements on brd0
  var f = brd0.create('functiongraph', [function(t){ return x0fix + v0fix*t + 0.5*a0fix*t*t }, 0, 10], 
                        {strokeColor:col1, strokeWidth:2 });
  var g = brd0.create('functiongraph', [function(t){ return sliderX.Value() + sliderV.Value()*t + 0.5*sliderA.Value()*t*t }, 0, 10], 
                        {strokeColor:col2, strokeWidth:2 });

  

  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return sliderX.Value(); });
  question.bindInput(1, () => { return sliderV.Value(); });
  question.bindInput(2, () => { return sliderA.Value(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDIDS, jsxCode, allowInputEntry=false); // use BOARDIDS here!!
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8370  -->
  <question type="formulas">
    <name>
      <text>Diagramme: v_x(t) aus a_x(t)-Daten</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper startet mit \({v0}\,\mathrm{m/s}\) entlang der \(x\)-Achse. Dann beschleunigt er 
<ul>
<li>für \({Dt1}\,\mathrm{s}\) mit \({a1}\,\mathrm{m/s^2}\),</li>
<li>für \({Dt2}\,\mathrm{s}\) mit \({a2}\,\mathrm{m/s^2}\) und</li>
<li>für \({Dt3}\,\mathrm{s}\) mit \({a3}\,\mathrm{m/s^2}\).</li>
</ul>
</p>
<p>Stellen Sie die Bewegung im \(v_x(t)\)-Diagramm richtig dar.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
aArray = shuffle([-3:3.1:0.1]);
v0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; v0 = round( v0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
vMax = 10; vMin = -10;

a1 = round( aArray[0] , 1);
for (j:[1:7]) { a1 = ( v0+a1*Dt1 > vMax) ? (a1 - 0.5) : ( ( v0+a1*Dt1 < vMin) ? (a1 + 0.5) : a1 ); }
Dv1 = round( a1*Dt1 , 1);
v1 = v0 + Dv1;

a2= round( aArray[1] , 1);
for (j:[1:7]) { a2 = ( v1+a2*Dt2 > vMax) ? (a2 - 0.5) : ( ( v1+a2*Dt2 < vMin) ? (a2 + 0.5) : a2 ); }
Dv2 = round( a2*Dt2 , 1);
v2 = v1 + Dv2;

a3 = round( aArray[2] , 1);
for (j:[1:7]) { a3 = ( v2+a3*Dt3 > vMax) ? (a3 - 0.5) : ( ( v2+a3*Dt3 < vMin) ? (a3 + 0.5) : a3 ); }
Dv3 = round( a3*Dt3 , 1);
v3 = v2 + Dv3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>7</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , v0,v1,v2,v3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_v0 = _3;
S_v1 = _4;
S_v2 = _5;
S_v3 = _6;

S_Dv1 = _4-_3;
S_Dv2 = _5-_4;
S_Dv3 = _6-_5;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );
crit_v0 = ( abs( S_v0 - v0 ) < 0.01 );

crit_Dv1 = ( abs( S_Dv1 - Dv1) <0.01 );
crit_Dv2 = ( abs( S_Dv2 - Dv2) <0.01 );
crit_Dv3 = ( abs( S_Dv3 - Dv3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + crit_v0 + 2*crit_Dv1 + 2*crit_Dv2 + 2*crit_Dv3 ) / 10;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="400" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.point.snapSizeX = 1;
  JXG.Options.point.snapSizeY = 0.1;
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  var t1,t2,t3 , v0,v1,v2,v3;
  [t1,t2,t3 , v0,v1,v2,v3] = 
    question.getAllValues([1,2,3 , 1,1,1,1 ]);

  
  // Create boards
  var brd0 = question.initBoard(BOARDID0, { 
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
      showCopyright: false, showNavigation: false 
    });
    

  // Define lines and points on brd0
  // Q: Is it necessary/beneficial/wrong to suspendUpdate here?
  brd0.suspendUpdate();
  var lV0 = brd0.create('line', [[0,-10], [0,10]], attrGlid);
  var pV0 = brd0.create('glider', [0,  v0, lV0], attrPmov() ),
      pV1 = brd0.create('point',  [t1, v1], attrPmov() ),
      pV2 = brd0.create('point',  [t2, v2], attrPmov() ),
      pV3 = brd0.create('point',  [t3, v3], attrPmov() );
  brd0.create('polygonalchain', [ pV0, pV1, pV2, pV3 ], attrLine);
  brd0.unsuspendUpdate();


  // Q: Are these updates necessary?
  // brd0.update();
  // brd1.update();

  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pV1.X(); });
  question.bindInput(1, () => { return pV2.X(); });
  question.bindInput(2, () => { return pV3.X(); });
  question.bindInput(3, () => { return pV0.Y(); });
  question.bindInput(4, () => { return pV1.Y(); });
  question.bindInput(5, () => { return pV2.Y(); });
  question.bindInput(6, () => { return pV3.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID0, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Im Zeitintervall \( \left[{t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \) beschleunigt der Körper um \( {Dv1}\,\mathrm{m/s} \) von \( {v0}\,\mathrm{m/s} \) auf \( {v1}\,\mathrm{m/s} \).</li>
<li>Im Zeitintervall \( \left[{t1}\,\mathrm{s}; {t2}\,\mathrm{s} \right] \) beschleunigt der Körper um \( {Dv2}\,\mathrm{m/s} \) von \( {v1}\,\mathrm{m/s} \) auf \( {v2}\,\mathrm{m/s} \).</li>
<li>Im Zeitintervall \( \left[{t2}\,\mathrm{s}; {t3}\,\mathrm{s} \right] \) beschleunigt der Körper um \( {Dv3}\,\mathrm{m/s} \) von \( {v2}\,\mathrm{m/s} \) auf \( {v3}\,\mathrm{m/s} \).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8371  -->
  <question type="formulas">
    <name>
      <text>Diagramme: v_x(t) aus a_x(t)-Diagramm</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper startet mit \({v0}\,\mathrm{m/s}\) entlang der \(x\)-Achse. Dann bewegt er sich gemäss des dargestellten \(a_x(t)\)-Graphs.</p>
<p>Stellen Sie die Bewegung im \(v_x(t)\)-Diagramm richtig dar.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
aArray = shuffle([-3:3.1:0.1]);
v0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; v0 = round( v0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
vMax = 10; vMin = -10;

a1 = round( aArray[0] , 1);
for (j:[1:7]) { a1 = ( v0+a1*Dt1 > vMax) ? (a1 - 0.5) : ( ( v0+a1*Dt1 < vMin) ? (a1 + 0.5) : a1 ); }
Dv1 = round( a1*Dt1 , 1);
v1 = v0 + Dv1;

a2= round( aArray[1] , 1);
for (j:[1:7]) { a2 = ( v1+a2*Dt2 > vMax) ? (a2 - 0.5) : ( ( v1+a2*Dt2 < vMin) ? (a2 + 0.5) : a2 ); }
Dv2 = round( a2*Dt2 , 1);
v2 = v1 + Dv2;

a3 = round( aArray[2] , 1);
for (j:[1:7]) { a3 = ( v2+a3*Dt3 > vMax) ? (a3 - 0.5) : ( ( v2+a3*Dt3 < vMin) ? (a3 + 0.5) : a3 ); }
Dv3 = round( a3*Dt3 , 1);
v3 = v2 + Dv3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>4</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>7</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , v0,v1,v2,v3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_v0 = _3;
S_v1 = _4;
S_v2 = _5;
S_v3 = _6;

S_Dv1 = _4-_3;
S_Dv2 = _5-_4;
S_Dv3 = _6-_5;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );
crit_v0 = ( abs( S_v0 - v0 ) < 0.01 );

crit_Dv1 = ( abs( S_Dv1 - Dv1) <0.01 );
crit_Dv2 = ( abs( S_Dv2 - Dv2) <0.01 );
crit_Dv3 = ( abs( S_Dv3 - Dv3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + crit_v0 + 2*crit_Dv1 + 2*crit_Dv2 + 2*crit_Dv3 ) / 10;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="400" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.point.snapSizeX = 1;
  JXG.Options.point.snapSizeY = 0.1;
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  var t1,t2,t3 , v0,v1,v2,v3;
  [t1,t2,t3 , v0,v1,v2,v3] = 
    question.getAllValues([1,2,3 , 1,1,1,1 ]);

  
  // Create boards
  var brd0 = question.initBoard(BOARDID0, { 
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
      showCopyright: false, showNavigation: false 
    });
    
  var brd1 = JXG.JSXGraph.initBoard(BOARDID1, { 
    boundingbox: [-1, 3.5, 12.5, -3.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$a_x\\;\\mathrm{(m/s^2)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
      showCopyright: false, showNavigation: false
    });
      
  
  // Define lines and points on brd1
  brd1.suspendUpdate();
  var pA1  = brd1.create('point', [{t1}, {a1}], attrPfix() ),
      pA2  = brd1.create('point', [{t2}, {a2}], attrPfix() ),
      pA3  = brd1.create('point', [{t3}, {a3}], attrPfix() ),
      pA01 = brd1.create('point', [0,    {a1}], attrPfix() ),
      pA12 = brd1.create('point', [{t1}, {a2}], attrPfix() ),
      pA23 = brd1.create('point', [{t2}, {a3}], attrPfix() );
  brd1.create('polygonalchain', [ pA01, pA1, pA12, pA2, pA23, pA3 ], attrLine);
  brd1.unsuspendUpdate();


  // Define lines and points on brd0
  // Q: Is it necessary/beneficial/wrong to suspendUpdate here?
  brd0.suspendUpdate();
  var lV0 = brd0.create('line', [[0,-10], [0,10]], attrGlid);
  var pV0 = brd0.create('glider', [0,  v0, lV0], attrPmov() ),
      pV1 = brd0.create('point',  [t1, v1], attrPmov() ),
      pV2 = brd0.create('point',  [t2, v2], attrPmov() ),
      pV3 = brd0.create('point',  [t3, v3], attrPmov() );
  brd0.create('polygonalchain', [ pV0, pV1, pV2, pV3 ], attrLine);
  brd0.unsuspendUpdate();


  // Q: Are these updates necessary?
  // brd0.update();
  // brd1.update();

  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pV1.X(); });
  question.bindInput(1, () => { return pV2.X(); });
  question.bindInput(2, () => { return pV3.X(); });
  question.bindInput(3, () => { return pV0.Y(); });
  question.bindInput(4, () => { return pV1.Y(); });
  question.bindInput(5, () => { return pV2.Y(); });
  question.bindInput(6, () => { return pV3.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID0, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Im Zeitintervall \( \left[{t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \) beschleunigt der Körper um \( {Dv1}\,\mathrm{m/s} \) von \( {v0}\,\mathrm{m/s} \) auf \( {v1}\,\mathrm{m/s} \).</li>
<li>Im Zeitintervall \( \left[{t1}\,\mathrm{s}; {t2}\,\mathrm{s} \right] \) beschleunigt der Körper um \( {Dv2}\,\mathrm{m/s} \) von \( {v1}\,\mathrm{m/s} \) auf \( {v2}\,\mathrm{m/s} \).</li>
<li>Im Zeitintervall \( \left[{t2}\,\mathrm{s}; {t3}\,\mathrm{s} \right] \) beschleunigt der Körper um \( {Dv3}\,\mathrm{m/s} \) von \( {v2}\,\mathrm{m/s} \) auf \( {v3}\,\mathrm{m/s} \).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8372  -->
  <question type="formulas">
    <name>
      <text>Verschiebung aus v_x(t)-Diagramm bestimmen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper bewegt sich geradlinig gemäss des dargestellten \(v_x(t)\)-Graphs.</p>

<p><jsxgraph width="400" height="400">

  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.point.snapToGrid = true;
  JXG.Options.point.snapSizeX = 0.1;
  JXG.Options.point.snapSizeY = 0.001;

  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';

  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:col1, size: 1};
    return { ...attr, ...addAttr}; 
  }
  const attrLine = {borders: {strokeColor:col1, strokeWidth: 3} };


  var brd = JXG.JSXGraph.initBoard(BOARDID, {
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
    zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright: false, showNavigation: false 
    });
  // console.log(brd);

  // Define elements on brd
  var pV0 = brd.create('point', [0   , {v0}], attrPfix() ),
      pV1 = brd.create('point', [{t1}, {v1}], attrPfix() ),
      pV2 = brd.create('point', [{t2}, {v2}], attrPfix() );
  brd.create('polygonalchain', [ pV0, pV1, pV2 ], attrLine);

</jsxgraph><br></p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4,5}; 
t2 = {6,7,8,9,10,11};

v0 = {1.0:10.1:0.2};
v1 = {1.0:10.1:0.2};
v2 = {-10.0:-1.1:0.2};</text>
</varsrandom>
<varsglobal><text>t0 = 0; 
v0 = round( v0 , 1 );
v1 = round( v1 , 1 );
v2 = round( v2 , 1 );

Dx1 = (v1 + v0) / 2 * (t1 - t0);
Dx2 = (v2 + v1) / 2 * (t2 - t1);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>2</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[Dx1, Dx2]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_Dx1 = _0;
S_Dx2 = _1;

crit_Dx1 = ( abs( S_Dx1 - Dx1 ) / abs(Dx1) < 0.05 ) + 0.5 * ( abs( -S_Dx1 - Dx1 ) / abs(Dx1) < 0.05 );
crit_Dx2 = ( abs( S_Dx2 - Dx2 ) / abs(Dx2) < 0.05 ) + 0.5 * ( abs( -S_Dx2 - Dx2 ) / abs(Dx2) < 0.05 );

crit_tot = 0.5*crit_Dx1 + 0.5*crit_Dx2 ;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welche Verschiebung erfährt der Körper in den ersten \({t1}\,\mathrm{s}\)?</p>
<p>&emsp; {_0} \(\mathrm{m}\)</p>

<p>Wie ändert sich die Position des Körpers im Zeitintervall zwischen \({t1}\,\mathrm{s}\) und \({t2}\,\mathrm{s}\)?</p>
<p>&emsp; {_1} \(\mathrm{m}\)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Bezugssystem</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8373  -->
  <question type="formulas">
    <name>
      <text>Relativgeschwindigkeiten von Autos</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Drei Autos fahren auf der Landstrasse. Der Tachometer
<ul>
<li>des roten Autos zeigt \({vR}\,\mathrm{km/h}\) an,</li>
<li>der des gelben Autos \({vG}\,\mathrm{km/h}\) und</li> 
<li>der des blauen Autos \({vBabs}\,\mathrm{km/h}\).</li>
</ul></p>

<img alt="" class="img-responsive" width="384px", height="97px" src="@@PLUGINFILE@@/Relativgeschwindigkeit.png"/>
<br><br>

<p>Geben Sie die <strong>Geschwindigkeiten</strong> (hier die \(x\)-Komponenten des Geschwindigkeitsvektors) der drei Autos im Bezugssystem des <strong>{colFrame}en</strong> Autos an.</p>]]></text>
<file name="Relativgeschwindigkeit.png" path="/" 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</file>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0010000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>vR = {85:103:2};
vG = {63:85:2};
vB = {-105:-63:2};

jCar = {0,1,2};</text>
</varsrandom>
<varsglobal><text><![CDATA[vBabs = abs(vB);

colFrame = ["rot","gelb","blau"][jCar];
vFrame = [vR, vG, vB][jCar];

vRrel = vR - vFrame;
vGrel = vG - vFrame;
vBrel = vB - vFrame;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>0.667</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>vRrel</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>km/h</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text>a) rotes Auto: {_0}{_u}</text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>0.667</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>vGrel</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>km/h</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text>b) gelbes Auto: {_0}{_u}</text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>0.667</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>vBrel</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>km/h</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text>c) blaues Auto: {_0}{_u}</text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8374  -->
  <question type="formulas">
    <name>
      <text>Relativgeschwindigkeiten von Vögeln</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Fünf Vögel fliegen mit den im ersten Diagramm dargestellten Geschwindigkeiten bezüglich des Bodens.</p>
<p>Verschieben Sie im zweiten Diagramm die Punkte, so dass die Geschwindigkeitsvektoren die Geschwindigkeiten bezüglich des Bezugssystems von <strong>Vogel {letter}</strong> angeben.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>vArray = shuffle([-15, -14, -13, -12, -11, -10, -9, -8, -7, -6, -5, -4, -3, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]);
j = {0:5};</text>
</varsrandom>
<varsglobal><text><![CDATA[letters = ["A", "B", "C", "D", "E"];

vA = vArray[0];
vB = vArray[1];
vC = vArray[2];
vD = vArray[3];
vE = vArray[4];

uA = vA - vArray[j];
uB = vB - vArray[j];
uC = vC - vArray[j];
uD = vD - vArray[j];
uE = vE - vArray[j];

letter = letters[j];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>5</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[uA, uB, uC, uD, uE]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_uA = _0;
S_uB = _1;
S_uC = _2;
S_uD = _3;
S_uE = _4;

crit_uA = ( abs( S_uA - uA ) < 0.1 );
crit_uB = ( abs( S_uB - uB ) < 0.1 );
crit_uC = ( abs( S_uC - uC ) < 0.1 );
crit_uD = ( abs( S_uD - uD ) < 0.1 );
crit_uE = ( abs( S_uE - uE ) < 0.1 );

crit_tot = ( crit_uA + crit_uB + crit_uC + crit_uD + crit_uE ) / 5;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="600" height="300" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  JXG.Options.point.showInfobox = false;
  JXG.Options.point.snapSizeX = 1;
  JXG.Options.point.snapToGrid = true;
  JXG.Options.arrow.lastArrow.size = 4;
  JXG.Options.arrow.lineCap = 'round';

  // Attributes for points
  function attrPvelV(addAttr={}) {
    const attr = {fixed:true, color:'none', withLabel:true, size:2, label:{ offset:[0,-14], anchorX:'middle' }};
    return { ...attr, ...addAttr};
  }
  function attrPvelU(addAttr={}) {
    const attr = {fixed:question.isSolved, withLabel:true, size:2, label:{ offset:[0,-14], anchorX:'middle' }};
    return { ...attr, ...addAttr};
  }
  const attrVecN = {fixed:question.isSolved, strokeColor:'#4285F4', strokeWidth:3};
  // const attrVecC = {fixed:question.isSolved, strokeColor:'#34A853', strokeWidth:2, dash:2};
  // const attrVecS = {fixed:question.isSolved, strokeColor:'#EA4335',  strokeWidth:4, };
  const attrPbodies = {fixed:true, size:4, color:'#34A853', label:{ offset:[0,14], anchorX:'middle' }};



  // Import final coordinates after submission
  var initialU = question.getAllValues([0,0,0,0,0]);
  
  const positions = [[40,1],[70,2],[60,3],[30,4],[50,5]];
  const initialV = [{vA},{vB},{vC},{vD},{vE}];
  

  // Create boards
  var brd0 = JXG.JSXGraph.initBoard(BOARDID0, { 
    boundingbox:[0, 7, 100, 0], axis:false, keepaspectratio:false,
    // zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright:false, showNavigation:false 
    });
    
  var brd1 = question.initBoard(BOARDID1, { 
    boundingbox:[0, 7, 100, 0], axis:false, keepaspectratio:false,
    // zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright:false, showNavigation:false 
    });
  

  // Define lines and points on brd0
  brd0.suspendUpdate();

  brd0.create('text', [50, 6.5, 'Bezugssystem «Boden»'], {anchorX:'middle', fontSize: 18});

  var pointsA = [];
  var pointsV = [];
  for (let i = 0; i != positions.length; i++) {
    pointsA[i] = brd0.create('point', positions[i], attrPbodies);
    pointsV[i] = brd0.create('point', [pointsA[i].X()+initialV[i], pointsA[i].Y()], 
        attrPvelV({name:function(){return (this.X()-pointsA[i].X()).toString().concat(' m/s');} }) );
    brd0.create('arrow', [pointsA[i], pointsV[i]], attrVecN );
  }

  brd0.unsuspendUpdate();


  // Define lines and points on brd1
  brd1.suspendUpdate();
 
  brd1.create('text', [50, 6.5, 'Bezugssystem «Vogel {letter}»'], {anchorX:'middle', fontSize: 18});

  var pointsB = [];
  var pointsU = [];
  for (let i = 0; i != positions.length; i++) {
    pointsB[i] = brd1.create('point', positions[i], attrPbodies);
    pointsU[i] = brd1.create('point', [pointsB[i].X()+initialU[i], pointsB[i].Y()], 
        attrPvelU({name:function(){return (this.X()-pointsB[i].X()).toString().concat(' m/s');} }) );
    pointsU[i].on('drag', function() { this.moveTo([this.X(), pointsB[i].Y()], 0); });
    brd1.create('arrow', [pointsB[i], pointsU[i]], attrVecN );
  }

  brd1.unsuspendUpdate();


  // Whenever the construction is altered the values of the points are sent to formulas.
  for (let i = 0; i != positions.length; i++) {
    question.bindInput(i, () => { return (pointsU[i].X()-pointsB[i].X()); });
  }

  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID1, jsxCode, allowInputEntry=true);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Beschleunigung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8377  -->
  <question type="formulas">
    <name>
      <text>Diagramme: a_x(t) aus v_x(t)-Diagramm</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper bewegt sich gemäss des dargestellten \(v_x(t)\)-Graphs.</p>
<p>Stellen Sie die Bewegung im \(a_x(t)\)-Diagramm richtig dar.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
aArray = shuffle([-3:3.1:0.1]);
v0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; v0 = round( v0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
vMax = 10; vMin = -10;

a1 = round( aArray[0] , 1);
for (j:[1:7]) { a1 = ( v0+a1*Dt1 > vMax) ? (a1 - 0.5) : ( ( v0+a1*Dt1 < vMin) ? (a1 + 0.5) : a1 ); }
Dv1 = round( a1*Dt1 , 1);
v1 = v0 + Dv1;

a2= round( aArray[1] , 1);
for (j:[1:7]) { a2 = ( v1+a2*Dt2 > vMax) ? (a2 - 0.5) : ( ( v1+a2*Dt2 < vMin) ? (a2 + 0.5) : a2 ); }
Dv2 = round( a2*Dt2 , 1);
v2 = v1 + Dv2;

a3 = round( aArray[2] , 1);
for (j:[1:7]) { a3 = ( v2+a3*Dt3 > vMax) ? (a3 - 0.5) : ( ( v2+a3*Dt3 < vMin) ? (a3 + 0.5) : a3 ); }
Dv3 = round( a3*Dt3 , 1);
v3 = v2 + Dv3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>4</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>6</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , a1,a2,a3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_a1 = _3;
S_a2 = _4;
S_a3 = _5;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );

crit_a1 = ( abs( S_a1 - a1) <0.01 );
crit_a2 = ( abs( S_a2 - a2) <0.01 );
crit_a3 = ( abs( S_a3 - a3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + 2*crit_a1 + 2*crit_a2 + 2*crit_a3 ) / 9;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="400" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.point.snapSizeX = 1;
  JXG.Options.point.snapSizeY = 0.1;
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  var t1,t2,t3 , a1,a2,a3;
  [t1,t2,t3 , a1,a2,a3] = 
    question.getAllValues([1,2,3 , 1,2,1]);
  
  // Create boards
  var brd0 = JXG.JSXGraph.initBoard(BOARDID0, { 
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
      showCopyright: false, showNavigation: false 
    });
    
  var brd1 = question.initBoard(BOARDID1, { 
    boundingbox: [-1, 3.5, 12.5, -3.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$a_x\\;\\mathrm{(m/s^2)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
      zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
      showCopyright: false, showNavigation: false
    });
  

  // Define lines and points on brd1
  brd1.suspendUpdate();
  var pA1 = brd1.create('point', [t1, a1], attrPmov({name: "pA1"}) ),
      pA2 = brd1.create('point', [t2, a2], attrPmov({name: "pA2"}) ),
      pA3 = brd1.create('point', [t3, a3], attrPmov({name: "pA3"}) ),
      pA01 = brd1.create('point', [0, "Y(pA1)"], attrPsma() ),
      pA12 = brd1.create('point', ["X(pA1)", "Y(pA2)"], attrPsma() ),
      pA23 = brd1.create('point', ["X(pA2)", "Y(pA3)"], attrPsma() );
  brd1.create('polygonalchain', [ pA01, pA1, pA12, pA2, pA23, pA3 ], attrLine);
  brd1.unsuspendUpdate();

  // Define lines and points on brd0
  brd0.suspendUpdate();
  var pV0 = brd0.create('point', [0   , {v0}], attrPfix() ),
      pV1 = brd0.create('point', [{t1}, {v1}], attrPfix() ),
      pV2 = brd0.create('point', [{t2}, {v2}], attrPfix() ),
      pV3 = brd0.create('point', [{t3}, {v3}], attrPfix() );
  brd0.create('polygonalchain', [ pV0, pV1, pV2, pV3 ], attrLine);
  brd0.unsuspendUpdate();




  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pA1.X(); });
  question.bindInput(1, () => { return pA2.X(); });
  question.bindInput(2, () => { return pA3.X(); });
  question.bindInput(3, () => { return pA1.Y(); });
  question.bindInput(4, () => { return pA2.Y(); });
  question.bindInput(5, () => { return pA3.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID1, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Im Zeitintervall \( \left[{t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \) beschleunigt der Körper mit \( {a1}\,\mathrm{m/s^2} \) (von \( {v0}\,\mathrm{m/s} \) auf \( {v1}\,\mathrm{m/s} \)).</li>
<li>Im Zeitintervall \( \left[{t1}\,\mathrm{s}; {t2}\,\mathrm{s} \right] \) beschleunigt der Körper mit \( {a2}\,\mathrm{m/s^2} \) (von \( {v1}\,\mathrm{m/s} \) auf \( {v2}\,\mathrm{m/s} \)).</li>
<li>Im Zeitintervall \( \left[{t2}\,\mathrm{s}; {t3}\,\mathrm{s} \right] \) beschleunigt der Körper mit \( {a3}\,\mathrm{m/s^2} \) (von \( {v2}\,\mathrm{m/s} \) auf \( {v3}\,\mathrm{m/s} \)).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8378  -->
  <question type="formulas">
    <name>
      <text>Geschwindigkeiten und Beschleunigung bestimmen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Die blaue Kurve im \(x(t)\)-Diagramm zeigt die gleichmässig beschleunigte Bewegung eines Körpers.</p>
<p>Die drei Punkte (blau, rot, grün) und damit die rote Sekante und grüne Tangente können Sie als Werkzeuge nutzen. Sie gehen nicht in die Bewertung ein.</p>

<div><jsxgraph width="500" height="400">

  JXG.Options.elements.highlight = false;
  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.infobox.fontSize = 13;
  JXG.Options.infobox.strokeColor = '#000';
  JXG.Options.point.snapToGrid = true;
  JXG.Options.point.snapSizeX = 0.1;
  JXG.Options.point.snapSizeY = 0.001;

  // Attributes for points and lines

  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';

  var brd = JXG.JSXGraph.initBoard(BOARDID, {
    boundingbox: [-1, 22, 7, -22], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } },
    zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright: false, showNavigation: false 
    });
  // console.log(brd);

  // Define elements on brd0
  function xFun(t){ return {X0val} + {V0val}*t + 0.5*{A0val}*t*t; }
  function vFun(t){ return {V0val} + {A0val}*t; }
  function tFun(t, tP){ return vFun(tP) * (t - tP) + xFun(tP); }

  const func = brd.create('functiongraph', [function(t){ return xFun(t) }, 0, 10], 
               {strokeColor:col1, strokeWidth:3 });
  
  const pT = brd.create('point', [2, tFun(2,1)], {withLabel:false, size: 2, color:col3 });   // Point on tangent
  const pG = brd.create('point', [4, xFun(4)], {withLabel:false, size: 2, color:col2 });    // Glider for secant
  const pP = brd.create('point', [1, xFun(1)], {withLabel:false, size: 2, color:col1 });      // Glider on function
  const pD = brd.create('point', [8, tFun(8,1)], {visible: false });                                       // Dummy for tangent

  pP.on('drag', function() { this.moveTo([this.X(), xFun(this.X())], 0); });
  pP.on('drag', function() { pT.moveTo([pT.X(), tFun(pT.X(), this.X())], 0); });
  pP.on('drag', function() { pD.moveTo([pD.X(), tFun(pD.X(), this.X())], 0); });
  pG.on('drag', function() { this.moveTo([this.X(), xFun(this.X())], 0); });
  pT.on('drag', function() { this.moveTo([this.X(), tFun(this.X(), pP.X())], 0); });
 
  brd.create('line', [pP,pG], {fixed: true, strokeColor:col2, strokeWidth:2 });                // Secant
  brd.create('line', [pP,pD], {fixed: true, strokeColor:col3, strokeWidth:2 });                // Tangent

</jsxgraph></div><br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>V0sign = {-1,1};
X0abs = {1.0:5.1:0.1};
V0abs = {2.0:4.1:0.1};
A0abs = {1.0:2.4:0.1};

t1 = {2.0:5.1:0.1};</text>
</varsrandom>
<varsglobal><text>X0val = round( +V0sign * X0abs , 1);
V0val = round( +V0sign * V0abs , 1);
A0val = round( -V0sign * A0abs , 1);


v0 = V0val;
v1 = round( V0val + A0val * t1 , 1);
vm = round( (V0val + v1) / 2 , 1);
am = A0val;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>4</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>4</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[v0, v1, vm, am]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_v0 = _0;
S_v1 = _1;
S_vm = _2;
S_am = _3;

Alt_vm = (S_v0 + S_v1) / 2;
Alt_am = (S_v1 - S_v0) / t1;

crit_v0 = ( abs( S_v0 - v0 ) < 0.11 );
crit_v1 = ( abs( S_v1 - v1 ) < 0.11 );
crit_vm = ( abs( S_vm - vm ) < 0.11 ) | ( abs( S_vm - Alt_vm ) < 0.11 );
crit_am = ( abs( S_am - am ) < 0.11 ) | ( abs( S_am - Alt_am ) < 0.11 );

crit_tot = 0.25*crit_v0 + 0.25*crit_v1 + 0.25*crit_vm + 0.25*crit_am ;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die Anfangsgeschwindigkeit der Bewegung?</p>
<p>&emsp; \(v_{x,0}=\;\) {_0} \(\mathrm{m/s}\)</p>

<p>Was ist die Momentangeschwindigkeit zum Zeitpunkt \(t_1={t1}\,\mathrm{s}\)?</p>
<p>&emsp; \(v_{x,1}=\;\) {_1} \(\mathrm{m/s}\)</p>

<p>Welche mittlere Geschwindigkeit hatte der Körper in den ersten  \({t1}\,\mathrm{s}\)?</p>
<p>&emsp; \(\overline{v}_x=\;\) {_2} \(\mathrm{m/s}\)</p>

<p>Welchen Wert hat die mittlere Beschleunigung in den ersten  \({t1}\,\mathrm{s}\)?</p>
<p>&emsp; \(a_x=\;\) {_3} \(\mathrm{m/s^2}\)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
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<text></text>
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<text></text>
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<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8379  -->
  <question type="formulas">
    <name>
      <text>Mittlere Beschleunigung aus v_x(t)-Diagramm</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Gegeben sei das folgende \(v_x(t)\)-Diagramm einer geradlinigen Bewegung eines Körpers.</p>

<br><jsxgraph width="600" height="300">

JXG.Options.elements.highlight = false;
JXG.Options.point.infoboxDigits = 1;
// JXG.Options.board.keepaspectratio= false;
// JXG.Options.board.axis= false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

const colRed = '#F44336', colPink = '#E91E63', colPurple = '#9C27B0', 
	  colBlue = '#2196F3', colIndigo = '#3F51B5', colCyan = '#00BCD4',
	  colGreen = '#4CAF50', colYellow = '#FFEB3B', colAmber = '#FFC107', 
	  colOrange = '#FF9800', colBrown = '#795548', colGrey = '#9E9E9E';


//-- Set global parameters for before and after submission


const element = document.getElementById("page");
const isMoodle = (typeof(element) != 'undefined' && element != null);

const X = isMoodle ? [{t0},{t1},{t2},{t3}] : [0,3,6,9];
const Y = isMoodle ? [{v0},{v1},{v2},{v3}] : [15,25,-10,0];
    

//-- Create boards and axes

const brdAttr = { boundingbox: [-1, 35, 10, -35], axis:true,
	defaultAxes: {
		x: {withLabel: true, name: 't in s',
			label: {position: 'rt', offset: [-0, 15], anchorX: 'right'} },
		y: {withLabel:true, name: 'v_x in m/s', 
			ticks: {ticksDistance: 5, insertTicks: false, minorTicks: 0},
			label: {position: 'rt', offset: [+15, -0]} } 
		}
	};

var brd0;
if (isMoodle) {
	brd0 = JXG.JSXGraph.initBoard(BOARDID, brdAttr);
} else {
	brd0 = JXG.JSXGraph.initBoard('jxgbox', brdAttr);
	console.log(brd0);
}

	 
// var xAxis = brd0.create('axis', [ [-21,0],[21,0] ], {
// 	straightFirst: false, straightLast: false,
// 	withLabel: true, name: 't in s', /* name: '$$x\\;\\mathrm{(m)}$$', */
// 	label: {position: 'rt', offset: [0, 15], anchorX: 'right', parse: true, fontSize: 12 },
// 	ticks: {ticksDistance: 10,  majorHeight: 10, tickEndings: [0,1],
// 		insertTicks: false, minorTicks: 1, minorHeight: ()=>10, drawZero: true,
// 		label: {visible: true, anchorX: 'middle', anchorY: 'top', offset: [0,-7] } }
// 	});
// var yAxis = brd0.create('axis', [ [0,0],[0,1.2] ], {
// 	straightFirst: false, straightLast: false,
// 	withLabel: true, name: 'x in m', /* name: '$$I/I_0$$', */
// 	label: {position: 'rt', offset: [12, -3], anchorX: 'left', parse: true, fontSize: 12 },
// 	ticks: {ticksDistance: 1, majorHeight: ()=>9*2*brd0.unitX, tickEndings: [0,0],
// 		insertTicks: false, minorTicks: 0, minorHeight: ()=>9*2*brd0.unitX, drawZero: true,
// 		label: {visible: false, anchorX: 'right', anchorY: 'middle', offset: [-3,0] } }
// 	}); 



//-- Objects in diagram


var p = [];

for (let j = 0; j != X.length; j++) {
	p.push(brd0.create('point', [X[j], Y[j]], {visible: false } ));
}

// brd0.create('polygonalchain', p, {borders: {strokeColor: colBlue, strokeWidth: 2 }});
const spl = brd0.create('spline', p, { strokeColor: colBlue, strokeWidth: 2 });


</jsxgraph><br>

<p>Geben Sie die gesuchten Grössen mit 1 Nachkommastelle (in m/s^2) und Einheit an.</p><br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrVelocities = shuffle([-25:26:5]);
arrDTimes = shuffle([2,3,4]);</text>
</varsrandom>
<varsglobal><text>v0 = arrVelocities[0];
v1 = arrVelocities[1];
v2 = arrVelocities[2];
v3 = arrVelocities[3];

Dv1 = v1 - v0;
Dv2 = v2 - v1;
Dv3 = v3 - v2;

Dt1 = arrDTimes[0];
Dt3 = arrDTimes[1];
t0 = 0;
t3 = 9;
t1 = t0 + Dt1;
t2 = t3 - Dt3;
Dt2= t2 - t1;


avAcc1 = round( Dv1 / Dt1 , 1 );
avAccTot = round( (v3 - v0) / (t3 - t0) , 1 );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
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  <text></text>
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  <text>1</text>
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  <text>0</text>
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  <text>1</text>
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 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avAcc1</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s^2</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die mittlere Beschleunigung im Zeitintervall \( \left[ {t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \)?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
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<text></text>
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<text></text>
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<answers>
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  <text>1</text>
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 </answermark>
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  <text>0</text>
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  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avAccTot</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s^2</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die mittlere Beschleunigung während der gesamten Bewegung?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
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    <tags>
      <tag><text>JXG</text>
</tag>
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  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Newton'sche Gesetze</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8384  -->
  <question type="formulas">
    <name>
      <text>Bart in der Rakete</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Bart (\({mBart}\,\mathrm{kg}\)) befindet sich in einer Rakete, welche fernab aller Himmelskörper mit \(a={aX}\,\mathrm{m/s^2}\) beschleunigt. Auch in der Rakete befindet sich eine Kugel von \({mKugel}\,\mathrm{kg}\), die an einer gedehnten Feder ruht.</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_Rakete.jpg" width="600px"/></p><br>]]></text>
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    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>6.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Die Antwort ist richtig.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Die Antwort ist teilweise richtig.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Die Antwort ist falsch.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>aX = {5.1:9.9:0.2};
mBart = {33:45:2};
mKugel = {11:20};

js = shuffle([0:4]);

DSpring = {21:48:2};</text>
</varsrandom>
<varsglobal><text><![CDATA[strFrame = ["Ja, es ist ein Inertialsystem.", "Nein, es ist kein Inertialsystem."];

strForces = ["\\(\\vec{F}_1\\)", "\\(\\vec{F}_2\\)", "\\(\\vec{F}_3\\)", "\\(\\vec{F}_4\\)"];
strForceFroms = ["der Kugel", "der Feder", "der Rakete"];
strForceOns = ["die Kugel", "die Feder", "die Rakete"];
jForceFroms = [0, 1, 1, 2];
jForceOns = [1, 0, 2, 1];
j0 = js[0];
jForceFrom0 = jForceFroms[j0];
jForceOn0 = jForceOns[j0];
j1 = js[1];
jForceFrom1 = jForceFroms[j1];
jForceOn1 = jForceOns[j1];

FBart = mBart * aX;

ySpring = mKugel * aX / DSpring;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>1</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>a) Ist das Bezugssystem von Bart bzw. der Rakete ein Inertialsystem? [1P]</p>
{_0:strFrame}]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>4</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[jForceFrom0, jForceOn0, jForceFrom1, jForceOn1]</text>
 </answer>
 <vars2>
  <text><![CDATA[crit0 = ( _0 == jForceFrom0 ) && ( _1 == jForceOn0 );
crit1 = ( _2 == jForceFrom1 ) && ( _3 == jForceOn1 );


# Calculate grade
grade = 0.5* crit0 + 0.5* crit1;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>b) Von welchen und auf welche Körper wirken die folgenden Kräfte? [1P]</p>
<p>&emsp;&emsp; Die Kraft {=strForces[j0]} wirkt von {_0:strForceFroms:MCE} auf {_1:strForceOns:MCE}.</p>
<p>&emsp;&emsp; Die Kraft {=strForces[j1]} wirkt von {_2:strForceFroms:MCE} auf {_3:strForceOns:MCE}.</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = FBart;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>N: m c h k M G;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>c) Welchen Wert hat die Normalkraft (von der Raketenwand) auf Bart? [2P]</p>
<p>&emsp;&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = ySpring;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>d) Die Feder (Federkonstante \({DSpring}\,\mathrm{N/m}\)) sorgt für die Beschleunigung der Kugel. Wie stark ist sie dadurch gedehnt? [2P]</p>
<p>&emsp;&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8385  -->
  <question type="formulas">
    <name>
      <text>Bart in der Rakete (ohne Federkraft)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Bart (\({mBart}\,\mathrm{kg}\)) befindet sich in einer Rakete, welche fernab aller Himmelskörper mit \(a={aX}\,\mathrm{m/s^2}\) beschleunigt. Auch in der Rakete befindet sich eine Kugel von \({mKugel}\,\mathrm{kg}\), die an einer gedehnten Feder ruht.</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_Rakete.jpg" width="600px"/></p><br>]]></text>
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    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>aX = {5.1:9.9:0.2};
mBart = {33:45:2};
mKugel = {11:20};

js = shuffle([0:4]);

DSpring = {21:48:2};</text>
</varsrandom>
<varsglobal><text><![CDATA[strFrame = ["Ja, es ist ein Inertialsystem.", "Nein, es ist kein Inertialsystem."];

strForces = ["\\(\\vec{F}_1\\)", "\\(\\vec{F}_2\\)", "\\(\\vec{F}_3\\)", "\\(\\vec{F}_4\\)"];
strForceFroms = ["der Kugel", "der Feder", "der Rakete"];
strForceOns = ["die Kugel", "die Feder", "die Rakete"];
jForceFroms = [0, 1, 1, 2];
jForceOns = [1, 0, 2, 1];
j0 = js[0];
jForceFrom0 = jForceFroms[j0];
jForceOn0 = jForceOns[j0];
j1 = js[1];
jForceFrom1 = jForceFroms[j1];
jForceOn1 = jForceOns[j1];

FBart = mBart * aX;

ySpring = mKugel * aX / DSpring;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>1</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>a) Ist das Bezugssystem von Bart bzw. der Rakete ein Inertialsystem? [1P]</p>
{_0:strFrame}]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>4</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[jForceFrom0, jForceOn0, jForceFrom1, jForceOn1]</text>
 </answer>
 <vars2>
  <text><![CDATA[crit0 = ( _0 == jForceFrom0 ) && ( _1 == jForceOn0 );
crit1 = ( _2 == jForceFrom1 ) && ( _3 == jForceOn1 );


# Calculate grade
grade = 0.5* crit0 + 0.5* crit1;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>b) Von welchen und auf welche Körper wirken die folgenden Kräfte? [1P]</p>
<p>&emsp;&emsp; Die Kraft {=strForces[j0]} wirkt von {_0:strForceFroms:MCE} auf {_1:strForceOns:MCE}.</p>
<p>&emsp;&emsp; Die Kraft {=strForces[j1]} wirkt von {_2:strForceFroms:MCE} auf {_3:strForceOns:MCE}.</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = FBart;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>N: m c h k M G;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>c) Welchen Wert hat die Normalkraft (von der Raketenwand) auf Bart? [2P]</p>
<p>&emsp;&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8386  -->
  <question type="formulas">
    <name>
      <text>Ein Traktor zieht drei Müllcontainer</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Traktor zieht reibungsfrei drei Müllcontainer zu je \( {m} \,\mathrm{kg} \). Dabei beschleunigt er in Fahrtrichtung mit \( {a} \,\mathrm{m/s^2} \).</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/fig_Traktor.png" width="200px"/></p><br>]]></text>
<file name="fig_Traktor.png" path="/" 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      <text>Your answer is correct.</text>
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      <text>Your answer is partially correct.</text>
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      <text>Your answer is incorrect.</text>
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    <shownumcorrect/>
<varsrandom><text>m = {60:100};
a = {1.1:2.0:0.1};</text>
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  <text>3*m*a</text>
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<text><![CDATA[<p>Welche Kraft wirkt vom Traktor auf Wagen 1? &emsp; {_0}{_u}]]></text>
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<answers>
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  <text>2*m*a</text>
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<text><![CDATA[<p>Welche Kraft wirkt von Wagen 1 auf Wagen 2? &emsp; {_0}{_u}]]></text>
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<text></text>
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<text><![CDATA[<p>Welche Kraft resultiert auf Wagen 2? &emsp; {_0}{_u}]]></text>
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<text></text>
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  </question>

<!-- question: 8387  -->
  <question type="formulas">
    <name>
      <text>Vier Klötze beschleunigt mit Reibung</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Vier gleiche Klötze mit einer Masse von je  \( {m}.0 \,\mathrm{kg} \) sind mit Seilen verbunden und gleiten über eine Ebene. Auf Klotz 1 wirkt eine Zugkraft nach rechts mit Betrag \( F_\mathrm{Z} = {FZ} \,\mathrm{N} \). Dadurch beschleunigen die Klötze mit \( {a} \,\mathrm{m/s^2} \).</p>

<p><br><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_KlotzketteVier.png" width="339px"/><br><br></p>]]></text>
<file name="tikz_KlotzketteVier.png" path="/" 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</file>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m = {1:10};
a = {1:10};
muGg = {1:10};

j1 = {1:5};
j2 = {0,1};
j3 = {1:5};</text>
</varsrandom>
<varsglobal><text>Fres = m * a;
FGR = muGg * m;
FS = Fres + FGR;
FZ = 4 * ( FS );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>Fres</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welchen Betrag hat die resultierende Kraft auf Klotz {j1}? [1P]</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text><![CDATA[strRopes = ["A", "B"];
nRopes = [3, 2];

strRope = strRopes[j2];
F = nRopes[j2] * FS;]]></text>
 </vars1>
 <answer>
  <text>F</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist die Seilkraft im Seil {strRope}? [1P]</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>FGR</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist die Gleitreibungskraft auf Klotz {j3}? [2P]</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8388  -->
  <question type="formulas">
    <name>
      <text>Vier Klötze beschleunigt ohne Reibung</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Vier gleiche Klötze der Masse  \( {m}.0 \,\mathrm{kg} \) sind mit Seilen verbunden und gleiten reibungsfrei über eine Ebene. Auf Klotz 1 wirkt eine Zugkraft nach rechts mit Betrag \( F_\mathrm{Z} = {FZ} \,\mathrm{N} \).</p>

<p><br><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_KlotzketteVier.png" width="339px"/><br><br></p>]]></text>
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</file>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m = {1:10};
a = {1:10};

j1 = {1:5};
j2 = {0,1};</text>
</varsrandom>
<varsglobal><text>Fres = m * a;
FZ = 4 * Fres;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>Fres</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welche resultierende Kraft wirkt auf Klotz {j1}? [1P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text><![CDATA[strRopes = ["A", "B"];
nRopes = [3, 2];

strRope = strRopes[j2];
F = nRopes[j2] * Fres;]]></text>
 </vars1>
 <answer>
  <text>F</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist die Seilkraft im Seil {strRope}? [1P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>a</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s^2</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist die Beschleunigung der Klötze? [1P]</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Würfe</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8395  -->
  <question type="formulas">
    <name>
      <text>Freier Fall: Zeit bis Tempo erreicht ist</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Fallschirmspringer verlässt ein Flugzeug in einer Höhe von 1400 m. Wir nehmen an, er fällt danach vollkommen frei.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>vEndKPH = {101:150:2};</text>
</varsrandom>
<varsglobal><text>g = 9.81;

t = (vEndKPH/3.6) / g;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = t;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Nach welcher Zeit erreicht er ein Tempo von {vEndKPH} km/h?</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8396  -->
  <question type="formulas">
    <name>
      <text>Horizontaler Wurf eines Balls im y(x)-Diagramm (mit Boden)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Projektil wird am Ort des Koordinatenursprungs horizontal mit \({velX}\,\mathrm{m/s}\) abgeschossen. Es fliegt dabei über einen Abhang mit einer Steigung von \({slope}\). Um herauszufinden, wo das Projektil aufschlägt, fertigen Sie ein \(y(x)\)-Diagramm an.</p>
<p>Gehen Sie hier von einem Ortsfaktor von \(10\,\mathrm{m/s^2}\) aus.<p>
<p>a) Verschieben Sie den rechten roten Punkt so, dass die blaue Parabel die Flugbahn des Projektils wiedergibt. [3P]</p>
<p>b) Verschieben Sie den linken roten Punkt so, dass die grüne Linie den Verlauf des abfallenden Bodens wiedergibt. [2P]</p>
<br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>5.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>time = {3,4,5,6};
velX = {30,35,40,45,50,55,60,65,70};
slope = {-0.2,-0.3,-0.4,-0.5,-0.6};</text>
</varsrandom>
<varsglobal><text>x1Line = velX * time;
y2Line = -60;

y1Sol = round( -5 * time * time );
x2Sol = round( y2Line / slope );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>5</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>2</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[y1Sol, x2Sol]</text>
 </answer>
 <vars2>
  <text><![CDATA[crit0 = ( abs( _0 - y1Sol ) < 1 );
crit1 = ( abs( _1 - x2Sol ) < 1 );

# Calculate grade
grade = 0.6* crit0 + 0.4* crit1;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="800" height="500" ext_formulas>

var jsxCode = function (question) {

  JXG.Options.elements.highlight = false;
  JXG.Options.point.snapToGrid = true;
  JXG.Options.point.snapSizeX = 10;
  JXG.Options.point.snapSizeY = 5;
  JXG.Options.point.infoboxDigits = 0;
  JXG.Options.infobox.fontSize = 13;
  JXG.Options.infobox.strokeColor = '#000';


  const col1 = '#4285F4', col2 = '#EA4335', col3 = '#34A853', col4 = '#FBBC05';
  const colVecN = '#4285F4', colVecS = '#EA4335', colVecC = '#34A853';

  // Attributes for points and lines
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false, color: col2};
    return { ...attr, ...addAttr};
  }
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color: col1, size: 1 };
    return { ...attr, ...addAttr };
  }
  const attrLine = {strokeColor: col1, strokeWidth: 3};

  // Import parameters
  const x1 = {x1Line}, y2 = {y2Line};
  // Import final coordinates after submission
  var y1, x2;
  [y1, x2] = question.getAllValues([0, 0]);
 
  
  var brd = question.initBoard(BOARDID, { 
    boundingbox: [-20, 20, 440, -280], axis: true, 
    defaultAxes: {
      x: {minorTicks: 1, withLabel: true, name: '$$x\\;\\mathrm{(m)}$$',
        label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 },
        ticks: {insertTicks: false, ticksDistance: 20, minorTicks: 0} },
      y: {withLabel: true, name: '$$y\\;\\mathrm{(m)}$$',
        label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12},
        ticks: {insertTicks: false, ticksDistance: 20, minorTicks: 0} }, },
    zoom: {enabled: false, wheel: false},
    pan: {enabled: false, needTwoFingers: false},
    showCopyright: false, showNavigation: false
  });
  console.log(brd);

  // Define elements on brd
  const x1Line = brd.create('line',[[x1,0],[x1,-10]], {visible: false});
  var x1Point = brd.create('glider',[0,y1,x1Line], attrPmov() );
  brd.create('functiongraph', [function(x) {return x1Point.Y()/x1Point.X()/x1Point.X()*x*x;}, 0, 440], attrLine );

  const y2Line = brd.create('line',[[0,y2],[10,y2]], {visible: false});
  var y2Point = brd.create('glider',[x2,0,y2Line], attrPmov() );
  brd.create('line',[[0,0],y2Point], {strokeColor: col3, strokeWidth: 3, straightFirst:false} );

  question.bindInput(0, () => { return x1Point.Y(); });
  question.bindInput(1, () => { return y2Point.X(); });

};

new JSXQuestion(BOARDID, jsxCode, allowInputEntry=false);

</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8397  -->
  <question type="formulas">
    <name>
      <text>Horizontaler Wurf eines Balls im y(x)-Diagramm (ohne Boden)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Projektil wird am Ort des Koordinatenursprungs horizontal mit \({velX}\,\mathrm{m/s}\) abgeschossen.</p>
<p>Verschieben Sie den rechten roten Punkt so, dass die blaue Parabel die Flugbahn des Projektils wiedergibt.</p>
<p>(Gehen Sie von einem Ortsfaktor von \(10\,\mathrm{m/s^2}\) aus.)</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>time = {3,4,5,6};
velX = {30,35,40,45,50,55,60,65,70};</text>
</varsrandom>
<varsglobal><text>x1Line = velX * time;

y1Sol = round( -5 * time * time );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[y1Sol]</text>
 </answer>
 <vars2>
  <text><![CDATA[crit0 = ( abs( _0 - y1Sol ) < 1 );

# Calculate grade
grade = 1* crit0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="800" height="500" ext_formulas>

var jsxCode = function (question) {

  JXG.Options.elements.highlight = false;
  JXG.Options.point.snapToGrid = true;
  JXG.Options.point.snapSizeX = 10;
  JXG.Options.point.snapSizeY = 5;
  JXG.Options.point.infoboxDigits = 0;
  JXG.Options.infobox.fontSize = 13;
  JXG.Options.infobox.strokeColor = '#000';


  const col1 = '#4285F4', col2 = '#EA4335', col3 = '#34A853', col4 = '#FBBC05';
  const colVecN = '#4285F4', colVecS = '#EA4335', colVecC = '#34A853';

  // Attributes for points and lines
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false, color: col2};
    return { ...attr, ...addAttr};
  }
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color: col1, size: 1 };
    return { ...attr, ...addAttr };
  }
  const attrLine = {strokeColor: col1, strokeWidth: 3};

  // Import parameters
  const x1 = {x1Line};
  // Import final coordinates after submission
  var y1;
  [y1] = question.getAllValues([0]);
 
  
  var brd = question.initBoard(BOARDID, { 
    boundingbox: [-20, 20, 440, -280], axis: true, 
    defaultAxes: {
      x: {minorTicks: 1, withLabel: true, name: '$$x\\;\\mathrm{(m)}$$',
        label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 },
        ticks: {insertTicks: false, ticksDistance: 20, minorTicks: 0} },
      y: {withLabel: true, name: '$$y\\;\\mathrm{(m)}$$',
        label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12},
        ticks: {insertTicks: false, ticksDistance: 20, minorTicks: 0} }, },
    zoom: {enabled: false, wheel: false},
    pan: {enabled: false, needTwoFingers: false},
    showCopyright: false, showNavigation: false
  });
  console.log(brd);

  // Define elements on brd
  const x1Line = brd.create('line',[[x1,0],[x1,-10]], {visible: false});
  var x1Point = brd.create('glider',[0,y1,x1Line], attrPmov() );
  brd.create('functiongraph', [function(x) {return x1Point.Y()/x1Point.X()/x1Point.X()*x*x;}, 0, 440], attrLine );

  question.bindInput(0, () => { return x1Point.Y(); });

};

new JSXQuestion(BOARDID, jsxCode, allowInputEntry=false);

</jsxgraph>]]></text>
 </subqtext>
 <feedback format="html">
<text><![CDATA[<p>Die Bewegungen des Projektils in horizontaler und vertikaler Richtung sind unabhängig voneinander.</p>
<p>In horizontaler Richtung bewegt sich das Projektil gleichförmig mit \({velX}\,\mathrm{m/s}\). Die Position \(x={x1Line}\,\mathrm{m}\) erreicht es zum Zeitpunkt \(t={time}.0\,\mathrm{s}\).</p>
<p>Zu diesem Zeitpunkt ist es vertikal (gleichmässig beschleunigt von der Schwerkraft) gefallen bis zur Position 
\[y=-\frac{1}{2}\,g\,t^2=-\frac{1}{2}\cdot 9.81\,\mathrm{m/s^2}\cdot({time}.0\,\mathrm{s})^2={y1Sol}\,\mathrm{m}.\]</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Reibung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8398  -->
  <question type="formulas">
    <name>
      <text>Gleitender Körper auf der schiefen Ebene</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Eine Kiste von \({mBox}\,\mathrm{kg}\) gleite eine schiefe Ebene hinab. Die Ebene sei um \({alpha}^\circ\) gegen die Horizontale geneigt. Der Gleitreibungskoeffizient für die Kiste auf dieser Ebene beträgt \({muG}\).</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>5.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>alpha = {11:46:2};
muG = {0.2:1:0.1};
mBox = {11:46:2};</text>
</varsrandom>
<varsglobal><text><![CDATA[strAccel = ["Die Kiste wird langsamer.", "Die Kiste wird schneller."];

g = 9.81;
FGparallel = mBox * g * sin(alpha/180*pi());
FGR = muG * mBox * g * cos(alpha/180*pi());

jAccel = ( FGparallel > FGR );]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = FGparallel;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>N</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>a) Welchen Betrag hat die Kraft, welche auf die Kiste hangabwärts wirkt? [2P]</p>
<p>&emsp;&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = FGR;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
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  <text>0.2</text>
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  <text>N</text>
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  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
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 <subqtext format="markdown">
<text><![CDATA[<p>b) Welchen Betrag hat die Gleitreibungskraft auf die Kiste? [2P]</p>
<p>&emsp;&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
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<text></text>
 </partiallycorrectfeedback>
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<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>jAccel</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
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  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>c) Wie bewegt sich die Kiste? [1P]</p>
{_0:strAccel}]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
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<text></text>
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<text></text>
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</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Energie und Arbeit</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8404  -->
  <question type="formulas">
    <name>
      <text>Arbeit, wenn Kind einen Wagen zieht</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Kind zieht einen Wagen mit einer Kraft von \({F}\,\mathrm{N}\) unter einem Winkel von \({alpha}^\circ\) zur Unterlage. Der Wagen bewegt sich dabei um \({s}\,\mathrm{m}\). </p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/fig_KindZiehtWagen.jpg?time=1656231029571" width="280px"/></p><br>]]></text>
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</file>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>F = {11:30:2};
s = {2.3:6:0.2};
alpha = {21:37:2};</text>
</varsrandom>
<varsglobal><text>W = F * s * cos(deg2rad(alpha));</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = W;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>J</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>J = Nm</text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist die Arbeit, die das Kind am Wagen verrichtet?</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8405  -->
  <question type="formulas">
    <name>
      <text>Baseballwurf: Arbeit aus Diagramm und kinetische Energie bestimmen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Werfer beim Baseball übt mit seinem Wurfarm eine horizontale Kraft auf den Ball aus. </p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/fig_Baseballwerfer.jpg" width="220px"/></p><br>]]></text>
<file name="fig_Baseballwerfer.jpg" path="/" 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</file>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>### Part 1
F = {110,120,130,140};
arrS = shuffle([0.2,0.4]);
s2 = {0.4,0.6};


### Part 2
W2 = {181:200:2};
mass = {143:180:2};</text>
</varsrandom>
<varsglobal><text>### Part 1
s1 = arrS[0];
s3 = arrS[1];
W1 = round( 0.5*F*s1 + F*s2 + 0.5*F*s3 );


### Part 2
v2 = sqrt( 2 * W2 / (mass/1000) );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = W1;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>J</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Der Verlauf der Kraft während des Ballwurfs ist näherungsweise im nachfolgenden Diagramm dargestellt.</p>

<br><p><jsxgraph width="500" height="300">

JXG.Options.point.showInfobox = false;

const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';

// Attributes for points and lines
const attrPfix = {fixed: true, visible: true, withLabel: false, color:col1, size: 1};
const attrLine = {borders: {strokeColor:col1, strokeWidth: 3} };

const F = {F}, s1 = {s1}, s2 = s1 + {s2}, s3 = s2 + {s3};

var brd = JXG.JSXGraph.initBoard(BOARDID, {
	boundingbox: [-0.1, 160, 1.4, -10], axis:true,
	defaultAxes: {
		x: {minorTicks: 1, withLabel: true, name: '$$s\\;\\mathrm{(m)}$$',
			label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 },
			ticks: {insertTicks: false, ticksDistance: 0.2, minorTicks: 1 } },
		y: {withLabel:true, name: '$$F_s\\;\\mathrm{(N)}$$',
			label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 },
			ticks: {insertTicks: false, ticksDistance: 20, minorTicks: 1 } },
		},
	zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
	showCopyright: false, showNavigation: false 
	});
//	console.log(brd);

// Define elements on brd
var p = [];

p.push(brd.create('point', [0 , 0], attrPfix ));
p.push(brd.create('point', [s1, F], attrPfix ));
p.push(brd.create('point', [s2, F], attrPfix ));
p.push(brd.create('point', [s3, 0], attrPfix ));

brd.create('polygonalchain', p, attrLine);

</jsxgraph></p><br>

<p>Bestimmen Sie mit Hilfe des Diagramms die Arbeit, die am Ball verrichtet wird. [2P]</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
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<text></text>
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</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = v2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie schnell wäre ein Ball von \({mass}\,\mathrm{g}\) beim Abwurf mit \({W2}\,\mathrm{J}\) kinetischer Energie? [2P]</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8406  -->
  <question type="formulas">
    <name>
      <text>Flaschenzug (n unbekannt)</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p><img src="@@PLUGINFILE@@/fig_FlaschenzugAufgabe5.png" alt="" width="252" height="420" role="presentation" class="img-responsive atto_image_button_right"></p>

<p>Mithilfe eines Flaschenzugs soll eine Last von \( F_\mathrm{L} = {FL} \,\mathrm{N} \) bei einer Zugkraft \( F_\mathrm{Z} = {FZ} \,\mathrm{N} \) um \( s_\mathrm{L} = {sL} \,\mathrm{cm} \) angehoben werden.</p>]]></text>
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</file>
    </questiontext>
    <generalfeedback format="html">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>FZ = {39.7:67.1:0.3};
n = {2:5};
sL = {11.1:19.9:0.2};</text>
</varsrandom>
<varsglobal><text>FL = n * FZ;
sZ = n * sL;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>n</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text>_err == 0</text>
 </correctness>
 <unitpenalty>
  <text>0.3</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie viele tragende Seile muss der Flaschenzug haben?</p>
<p>&emsp;{_0}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>sZ</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.02]]></text>
 </correctness>
 <unitpenalty>
  <text>0.3</text>
 </unitpenalty>
 <postunit>
  <text>cm</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie weit muss dazu am Seil gezogen werden?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text></text>
 </feedback>
 <correctfeedback format="html">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="html">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="html">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8407  -->
  <question type="formulas">
    <name>
      <text>Spannenergie in einer Feder</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Wenn man auf eine Feder mit \({FF}\,\mathrm{N}\) einwirkt, dehnt sie sich um \({y}\,\mathrm{m}\).</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>FF = {2.1:5:0.2};
y = {0.11:0.4:0.02};</text>
</varsrandom>
<varsglobal><text>EF = 0.5 * FF * y;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = EF;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>J</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie gross ist in diesem Fall die Spannenergie in der Feder?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8408  -->
  <question type="formulas">
    <name>
      <text>Tempo von Luftmolekülen bei Raumtemperatur</text>
    </name>
    <questiontext format="markdown">
      <text><![CDATA[<p>Bei Zimmertemperatur hat ein {=strMoleculeName[j]} mit einer Masse von {=numMoleculeMass[j]} kg typischerweise eine kinetische Energie von ca. {Etherm}E-21 J.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>Etherm = {6.07:6.22:0.02};
j = {0:5};</text>
</varsrandom>
<varsglobal><text><![CDATA[strMoleculeName = ["Sauerstoffmolekül", "Stickstoffmolekül", "Kohlenstoffdioxidmolekül", "Heliumatom", "Neonatom"];
numMoleculeMass = [5.31E-26, 4.65E-26, 7.31E-26, 6.64E-27, 3.32E-26];

speed = sqrt( 2* Etherm * 1E-21 / numMoleculeMass[j] );]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = speed;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}
nSigFigAns = max( nSigFig, nSigFigAns);

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie schnell bewegt es sich?</p>
<p>&emsp;{_0}{_u}&emsp; (Angabe mit Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8409  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung: Kinetische Energie</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>In einem gegebenen Bezugssystem besitze ein Körper eine gewisse kinetische Energie.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrJs = shuffle( [0:8] );</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "gefünftelt" , "geviertelt" , "gedrittelt" , "halbiert" , "verdoppelt" , "verdreifacht" , "vervierfacht" , "verfünffacht" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[0];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j]**2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die kinetische Energie des Körpers, wenn seine <u>Geschindigkeit</u> {strFact} wird?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[1];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j];
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor würde sich die kinetische Energie des Körpers unterscheiden, wenn seine <u>Masse</u> {strFact} würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[2];

strFact = factNames[j];

nSigFig = 2;
origSol = sqrt(factNumbers[j]);
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor müsste man die Geschwindigkeit des Körpers verändern, damit seine kinetische  Energie {strFact} wird?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j1 = arrJs[3];
j2 = arrJs[4];

strFact1 = factNames[j1];
strFact2 = factNames[j2];

nSigFig = 2;
origSol = factNumbers[j1] * factNumbers[j2]**2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor würde sich die kinetische Energie des Körpers hypothetisch verändern, wenn gleichzeitig seine <u>Masse</u> {strFact1} und  seine <u>Geschwindigkeit</u> {strFact2} würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8410  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung: Potenzielle Energie</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>In einem gegebenen Bezugssystem besitze ein Körper eine gewisse potenzielle Energie.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrJs = shuffle( [0:8] );</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "gefünftelt" , "geviertelt" , "gedrittelt" , "halbiert" , "verdoppelt" , "verdreifacht" , "vervierfacht" , "verfünffacht" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[0];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j];
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die potenzielle Energie des Körpers, wenn seine <u>Höhe</u> {strFact} wird?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[1];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j];
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor würde sich die potenzielle Energie des Körpers unterscheiden, wenn seine <u>Masse</u> {strFact} würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[2];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j];
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor müsste man die Höhe des Körpers verändern, damit seine potenzielle  Energie {strFact} wird?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j1 = arrJs[3];
j2 = arrJs[4];

strFact1 = factNames[j1];
strFact2 = factNames[j2];

nSigFig = 2;
origSol = factNumbers[j1] * factNumbers[j2];
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor würde sich die potenzielle Energie des Körpers hypothetisch verändern, wenn gleichzeitig seine <u>Masse</u> {strFact1} und  seine <u>Höhe</u> {strFact2} würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8411  -->
  <question type="formulas">
    <name>
      <text>Verhältnisbildung: Spannenergie</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>In einer Hooke'schen Feder sei eine gewisse Spannenergie vorhanden.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrJs = shuffle( [0:8] );</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "gefünftelt" , "geviertelt" , "gedrittelt" , "halbiert" , "verdoppelt" , "verdreifacht" , "vervierfacht" , "verfünffacht" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[0];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j]**2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Um welchen Faktor ändert sich die Spannenergie, wenn die <u>Federdehnung</u> {strFact} wird?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[1];

strFact = factNames[j];

nSigFig = 2;
origSol = factNumbers[j];
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor würde sich die Spannenergie hypothetisch unterscheiden, wenn die <u>Federkonstante</u> {strFact} würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[2];

strFact = factNames[j];

nSigFig = 2;
origSol = sqrt(factNumbers[j]);
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor müsste man die Federdehnung verändern, damit die Spannenergie {strFact} wird?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j1 = arrJs[3];
j2 = arrJs[4];

strFact1 = factNames[j1];
strFact2 = factNames[j2];

nSigFig = 2;
origSol = factNumbers[j1] * factNumbers[j2]**2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
  divTest = ( round( 10**(i+1) * abs(mantAns) , 6 ) ) % 10;
  nSigFigAns = ( divTest > 0.5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct solution with respect to significant figures
solSFA = sigfig( origSol , nSigFigAns );
critSolCorrect = ( abs(origAns - solSFA) / solSFA < 0.01 );

# Solution with rounding errors with respect to significant figures
critSolRound1 = ( abs(origAns - solSFA) / solSFA < 0.02 );

# Calculate grade
critSol = max( 1.0* critSolCorrect, 0.8*critSolRound1 );
grade = ( critSol > 0 ) ? critSol : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor würde sich die Spannenergie hypothetisch verändern, wenn gleichzeitig die <u>Federkonstante</u> {strFact1} und  die <u>Federdehnung</u> {strFact2} würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit {nSigFig} signifikanten Stellen)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Energieerhaltung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8431  -->
  <question type="formulas">
    <name>
      <text>Energieumwandler</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>In den folgenden Beispielen wird immer eine bestimmte Eingangs-Energieform in eine Ausgangs-Energieform umgewandelt. Wählen Sie jeweils die richtigen Energieformen aus.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrJs = shuffle([0:14]);</text>
</varsrandom>
<varsglobal><text><![CDATA[strDevices = ["der Bergabfahrt in einer Achterbahn", "einem Dynamo / Generator", "einem Wasserfall", "einer Armbrust", "einem Elektromotor", "einem Wasserkocher", "dem Laden eines Akkus", "einer Leuchtdiode", "der Entladung eines Akkus", "einem Feuer", "einem Kernkraftwerk", "einer Solarzelle", "einer Solarthermieanlage", "der Photosynthese in Planzen"];
strEnergies = ["kinetische Energie", "potentielle Energie", "Spannenergie", "elektrische Energie", "thermische Energie", "chemische Energie", "Kernenergie", "Strahlungsenergie"];

solIns    = [1, 0, 1, 2, 3, 3, 3, 3, 5, 5, 6, 7, 7, 7];
solOuts = [0, 3, 0, 0, 0, 4, 5, 7, 3, 4, 3, 3, 4, 5];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>6</text>
 </numbox>
 <vars1>
  <text>solIn0 = solIns[arrJs[0]];
solOut0 = solOuts[arrJs[0]];

solIn1 = solIns[arrJs[1]];
solOut1 = solOuts[arrJs[1]];

solIn2 = solIns[arrJs[2]];
solOut2 = solOuts[arrJs[2]];</text>
 </vars1>
 <answer>
  <text>[solIn0, solOut0, solIn1, solOut1, solIn2, solOut2]</text>
 </answer>
 <vars2>
  <text>grade0 = 0.5*( _0 == solIn0) + 0.5*( _1 == solOut0);
grade1 = 0.5*( _2 == solIn1) + 0.5*( _3 == solOut1);
grade2 = 0.5*( _4 == solIn2) + 0.5*( _5 == solOut2);

grade = 0.333*grade0 + 0.333*grade1 + 0.334*grade2;</text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Bei {=strDevices[arrJs[0]]} wird {_0:strEnergies:MCE} umgewandelt in {_1:strEnergies:MCE}.</p>
<p>Bei {=strDevices[arrJs[1]]} wird {_2:strEnergies:MCE} umgewandelt in {_3:strEnergies:MCE}.</p><p>Bei {=strDevices[arrJs[2]]} wird {_4:strEnergies:MCE} umgewandelt in {_5:strEnergies:MCE}.</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8432  -->
  <question type="formulas">
    <name>
      <text>Orange und grüne Person auf dem Schleuderbrett</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Beim Schleuderbrett wird im Idealfall die ganze mechanische Energie von einer Person auf die andere übertragen.</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/fig_Schleuderbrett.jpg" width="180px"/></p><br>

<p>Was lässt sich sagen, wenn die grüne Person die {strMass} Masse der orangen Person besitzt?</p>]]></text>
<file name="fig_Schleuderbrett.jpg" path="/" 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    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>1.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>j = {0,2};</text>
</varsrandom>
<varsglobal><text><![CDATA[strMasses = ["halbe", "gleiche", "doppelte"];
strHeights = ["ein Viertel so", "halb so", "gleich", "doppelt so", "viermal so"];

strMass = strMasses[j];

jHeight = 3 - j;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
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  <text></text>
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 <numbox>
  <text>1</text>
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 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>jHeight</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Die grüne Person kommt &ensp;{_0: strHeights:MCE}&ensp; hoch wie die orange Person.</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8433  -->
  <question type="formulas">
    <name>
      <text>Totale mechanische Energie bei freiem Fall</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper von \({mass}\,\mathrm{kg}\) wird aus \({height}\,\mathrm{m}\) Höhe über dem Boden ohne Anfangsgeschwindigkeit fallen gelassen. Luftwiderstand ist zu vernachlässigen.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>mass = {1.1:2:0.2};
height = {2.1:5:0.2};</text>
</varsrandom>
<varsglobal><text>E = sigfig( mass * 9.81 * height , 2);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
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  <text></text>
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 <answermark>
  <text>2</text>
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 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>E</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.5]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>J</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie gross ist seine totale mechanische Energie (bezüglich dem Boden), nachdem er \(1.0\,\mathrm{m}\) gefallen ist?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8434  -->
  <question type="formulas">
    <name>
      <text>Wagen rollt von Hang in Federpuffer hinein</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Wagen rollt aus der Ruhe reibungsfrei einen Hang von \(1.0\,\mathrm{m}\) Höhe hinunter und in eine Feder hinein. Er drückt die Feder um 1 cm zusammen (bevor sie ihn wieder wegschleudert).</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_WaggonRampeFeder.png" width="360px"/></p><br>]]></text>
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      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrJs = shuffle( [0:8] );</text>
</varsrandom>
<varsglobal><text><![CDATA[factNames = [ "ein Fünftel" , "ein Viertel" , "ein Drittel" , "halb" , "doppelt" , "dreimal" , "viermal" , "fünf mal" ];
factNumbers = [ 1/5 , 1/4 , 1/3 , 1/2 , 2 , 3 , 4 , 5 ];]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
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  <text></text>
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  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[0];

strFact = factNames[j];
sol = sigfig( sqrt(factNumbers[j]) ,2);</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.05]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor änderte sich die maximale Federdehnung, wenn der Hang {strFact} so hoch wäre?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit zwei signifikanten Stellen eingeben)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>j = arrJs[1];

strFact = factNames[j];
sol = sigfig( factNumbers[j]**2 ,2);</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.05]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Um welchen Faktor müsste die Masse des Wagens geändert werden, damit die maximale Federdehnung {strFact} so gross würde?</p>
<p>&emsp;{_0}&emsp; (Dezimalzahl mit zwei signifikanten Stellen eingeben)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
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<text></text>
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 <partiallycorrectfeedback format="markdown">
<text></text>
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 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Leistung und Wirkungsgrad</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8454  -->
  <question type="formulas">
    <name>
      <text>Beschleunigungsleistung eines Sportwagens</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Sportwagen der Masse \({m}\,\mathrm{kg}\) beschleunigt in \({Dt}\,\mathrm{s}\) auf \(100\,\mathrm{km/h}\).</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>m = {1400:2501:100};
Dt = {2.7:5.6:0.2};</text>
</varsrandom>
<varsglobal><text>P = sigfig( 0.5 * m * 27.8**2 / Dt , 2);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>P</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>W</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>W: k; W: M;</text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Welcher mittleren Beschleunigungsleistung entspricht das?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8455  -->
  <question type="formulas">
    <name>
      <text>Wirkungsgradkette vom Kraftwerk zum Gerät</text>
    </name>
    <questiontext format="markdown">
      <text><![CDATA[<p>Die Abbildung zeigt eine Kette mehrerer Energieumwandlungen von einer primären Energiequelle bis zur gewünschten Energieform bei der Nutzung eines Endgeräts.</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_Wirkungsgradkette.png" width="700px"/></p><br>

<p>Folgende Grössen sind bekannt:</p>
<ul>
  <li>Die dem Kraftwerk zugeführte Leistung beträgt \(P_\mathrm{zu}={P_zu_kW}\,\mathrm{kW}\).</li>
  <li>Der Wirkungsgrad des Kraftwerks ist \(\eta_\mathrm{K}={eta_K}\).</li>
  <li>Der Wirkungsgrad des Geräts ist \(\eta_\mathrm{G}={eta_G}\).</li>
  <li>Die Nutzleistung des Geräts beträgt \(P_\mathrm{G}={P_G_kW}\,\mathrm{kW}\).</li>
</ul>]]></text>
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</file>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>P_G_kW = {1.13:3.60:0.02};

eta_G = {0.81:1:0.02};
eta_T = {0.89:1:0.02};
eta_K = {0.35:0.6:0.02};</text>
</varsrandom>
<varsglobal><text>### Gegebene Grössen

P_zu_kW = sigfig( P_G_kW / (eta_K * eta_T * eta_G) , 3 );
P_zu_kW = ( P_zu_kW == sigfig(P_zu_kW,2) ) ? P_zu_kW +0.01 : P_zu_kW;

### Gesuchte Grössen

P_G_PS = P_G_kW * 1000 / 736;

E_G_kWh = P_G_kW * 24 ;

P_KVerlust_kW = P_zu_kW * (1 - eta_K) ;

eta_T = P_G_kW / P_zu_kW / eta_G / eta_K ;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = P_G_PS;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie vielen PS entspricht die Nutzleistung des Geräts?</p>
<p>&emsp;{_0}&ensp;\(\mathrm{PS}\)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 3;
origSol = E_G_kWh;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie viel Energie wandelt das Gerät an einem Tag im Dauerbetrieb um?</p>
<p>&emsp;{_0}&ensp;\(\mathrm{kWh}\)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = P_KVerlust_kW;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>kW</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Wie viel Leistung geht im Kraftwerk «verloren»?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
origSol = eta_T;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Was ist der Wirkungsgrad beim Transport der elektrischen Energie?</p>
<p>&emsp;{_0}&emsp;(Eingabe als Dezimalzahl, nicht in Prozent)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 8456  -->
  <question type="formulas">
    <name>
      <text>Zugeführte Leistung einer Maschine berechnen</text>
    </name>
    <questiontext format="markdown">
      <text><![CDATA[<p>Eine Maschine erbringt eine Leistung von \({Pnutz}\,\mathrm{MW}\) bei einem Wirkungsgrad von \({eta}\rm{\%}\).</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>Pnutz = {5.1:9.8:0.2};
eta = {51:89:2};</text>
</varsrandom>
<varsglobal><text>Pzu = sigfig( Pnutz / eta * 100 , 2);
Pverlust = sigfig( Pzu - Pnutz , 2);

PzuSol = Pzu * 10**6;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>PzuSol</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.01]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>W</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text>W: M;</text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Berechnen Sie die zugeführte Leistung.</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text>\[ P_\mathrm{zu} = \frac{P_\mathrm{nutz}}{\eta} = \frac{{Pnutz}\,\mathrm{MW}}{{eta}\%} = {Pzu}\,\mathrm{MW} \]</text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Geschwindigkeit</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8627  -->
  <question type="formulas">
    <name>
      <text>Diagramme: v_x(t) aus x(t)-Diagramm</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Ein Körper bewegt sich gemäss des dargestellten \(x(t)\)-Graphs.</p>
<p>Stellen Sie die Bewegung im \(v_x(t)\)-Diagramm richtig dar.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>t1 = {1,2,3,4}; t2 = {5,6,7}; t3 = {8,9,10,11};
vArray = shuffle([-3:3.1:0.1]);
x0 = {-10:10.1:0.1};</text>
</varsrandom>
<varsglobal><text><![CDATA[t0 = 0; x0 = round( x0 , 1 );
Dt1 = t1 - t0; Dt2 = t2-t1; Dt3 = t3-t2;
xMax = 10; xMin = -10;

v1 = round( vArray[0] , 1);
for (j:[1:7]) { v1 = ( x0+v1*Dt1 > xMax) ? (v1 - 0.5) : ( ( x0+v1*Dt1 < xMin) ? (v1 + 0.5) : v1 ); }
Dx1 = round( v1*Dt1 , 1);
x1 = x0 + Dx1;

v2= round( vArray[1] , 1);
for (j:[1:7]) { v2 = ( x1+v2*Dt2 > xMax) ? (v2 - 0.5) : ( ( x1+v2*Dt2 < xMin) ? (v2 + 0.5) : v2 ); }
Dx2 = round( v2*Dt2 , 1);
x2 = x1 + Dx2;

v3 = round( vArray[2] , 1);
for (j:[1:7]) { v3 = ( x2+v3*Dt3 > xMax) ? (v3 - 0.5) : ( ( x2+v3*Dt3 < xMin) ? (v3 + 0.5) : v3 ); }
Dx3 = round( v3*Dt3 , 1);
x3 = x2 + Dx3;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>6</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[t1,t2,t3 , v1,v2,v3]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_t1 = _0;
S_t2 = _1;
S_t3 = _2;
S_v1 = _3;
S_v2 = _4;
S_v3 = _5;

crit_t1 = ( abs( S_t1 - t1 ) < 0.01 );
crit_t2 = ( abs( S_t2 - t2 ) < 0.01 );
crit_t3 = ( abs( S_t3 - t3 ) < 0.01 );

crit_v1 = ( abs( S_v1 - v1) <0.01 );
crit_v2 = ( abs( S_v2 - v2) <0.01 );
crit_v3 = ( abs( S_v3 - v3) <0.01 );

crit_tot = ( crit_t1 + crit_t2 + crit_t3 + 2*crit_v1 + 2*crit_v2 + 2*crit_v3 ) / 9;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="400" height="400" numberOfBoards="2" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
JXG.Options.elements.highlight = false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};
  
JXG.Options.point.infoboxDigits = 1;
JXG.Options.point.snapSizeX = 1;
JXG.Options.point.snapSizeY = 0.1;
  
  // Attributes for points and lines
  function attrPfix(addAttr={}) {
    const attr = {fixed: true, visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr}; 
  }
  function attrPmov(addAttr={}) {
    const attr = {fixed: question.isSolved, snapToGrid: true, withLabel: false};
    return { ...attr, ...addAttr};
  }
  function attrPsma(addAttr={}) {
    const attr = {visible: true, withLabel: false, color:'#4285F4', size: 1};
    return { ...attr, ...addAttr};
  }
  const attrLine = {borders: {strokeColor:'#4285F4', strokeWidth: 3} };
  const attrGlid = {visible:false};


  // Import final coordinates after submission
  var t1,t2,t3 , v1,v2,v3;
  [t1,t2,t3 , v1,v2,v3] = 
    question.getAllValues([1,2,3 , 1,2,1]);
  
  // Create boards
  var brd0 = JXG.JSXGraph.initBoard(BOARDID0, { 
    boundingbox: [-1, 11.5, 12.5, -11.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
    });
    
  var brd1 = question.initBoard(BOARDID1, { 
    boundingbox: [-1, 3.5, 12.5, -3.5], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$v_x\\;\\mathrm{(m/s)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
    });
  

  // Define lines and points on brd1
  brd1.suspendUpdate();
  var pV1 = brd1.create('point', [t1, v1], attrPmov({name: "pV1"}) ),
      pV2 = brd1.create('point', [t2, v2], attrPmov({name: "pV2"}) ),
      pV3 = brd1.create('point', [t3, v3], attrPmov({name: "pV3"}) ),
      pV01 = brd1.create('point', [0, "Y(pV1)"], attrPsma() ),
      pV12 = brd1.create('point', ["X(pV1)", "Y(pV2)"], attrPsma() ),
      pV23 = brd1.create('point', ["X(pV2)", "Y(pV3)"], attrPsma() );
  brd1.create('polygonalchain', [ pV01, pV1, pV12, pV2, pV23, pV3 ], attrLine);
  brd1.unsuspendUpdate();

  // Define lines and points on brd0
  brd0.suspendUpdate();
  var pX0 = brd0.create('point', [0   , {x0}], attrPfix() ),
      pX1 = brd0.create('point', [{t1}, {x1}], attrPfix() ),
      pX2 = brd0.create('point', [{t2}, {x2}], attrPfix() ),
      pX3 = brd0.create('point', [{t3}, {x3}], attrPfix() );
  brd0.create('polygonalchain', [ pX0, pX1, pX2, pX3 ], attrLine);
  brd0.unsuspendUpdate();




  // Whenever the construction is altered the values of the points are sent to formulas.
  question.bindInput(0, () => { return pV1.X(); });
  question.bindInput(1, () => { return pV2.X(); });
  question.bindInput(2, () => { return pV3.X(); });
  question.bindInput(3, () => { return pV1.Y(); });
  question.bindInput(4, () => { return pV2.Y(); });
  question.bindInput(5, () => { return pV3.Y(); });
  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID1, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
 </subqtext>
 <feedback format="markdown">
<text><![CDATA[<h4>Lösung:</h4>
<ul>
<li>Im Zeitintervall \( \left[{t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \) bewegt sich der Körper mit \( {v1}\,\mathrm{m/s} \) (von \( {x0}\,\mathrm{m} \) nach \( {x1}\,\mathrm{m} \)).</li>
<li>Im Zeitintervall \( \left[{t1}\,\mathrm{s}; {t2}\,\mathrm{s} \right] \) bewegt sich der Körper mit \( {v2}\,\mathrm{m/s} \) (von \( {x1}\,\mathrm{m} \) nach \( {x2}\,\mathrm{m} \)).</li>
<li>Im Zeitintervall \( \left[{t2}\,\mathrm{s}; {t3}\,\mathrm{s} \right] \) bewegt sich der Körper mit \( {v3}\,\mathrm{m/s} \) (von \( {x2}\,\mathrm{m} \) nach \( {x3}\,\mathrm{m} \)).</li>
</ul>
</p>]]></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8628  -->
  <question type="formulas">
    <name>
      <text>Mittlere Geschwindigkeit und Tempo aus x(t)-Diagramm bestimmen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Gegeben sei das folgende \(x(t)\)-Diagramm für die geradlinige, abschnittsweise gleichförmige Bewegung eines Körpers.</p>

<br><jsxgraph width="400" height="300">
var brd = JXG.JSXGraph.initBoard(BOARDID, {
    boundingbox: [-1, 35, 8, -15], axis:true,
    defaultAxes: {
        x: {withLabel: true, name: 't in s',
            label: {position: 'rt', offset: [-0, 15], anchorX: 'right'} },
        y: {withLabel:true, name: 'x in m',      
            label: {position: 'rt', offset: [+15, -0]} } },
        zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
        showCopyright: false, showNavigation: false
     });

var X = [{t0},{t1},{t2},{t3}];
var Y = [{x0},{x1},{x2},{x3}];
brd.create('curve', [X,Y], {strokeColor:'#4285F4', strokeWidth:2});

</jsxgraph><br>

<p>Geben Sie die gesuchten Grössen mit drei signifikanten Stellen (und Einheit) an.</p><br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>x0 = {-10:31:5};
x1 = {-10:31:5};
x2 = {-10:31:5};
x3 = {-10:31:5};

Dt1 = {1:4};
Dt3 = {1:4};</text>
</varsrandom>
<varsglobal><text>Dx1 = x1 - x0;
Dx2 = x2 - x1;
Dx3 = x3 - x2;
t0 = 0;
t3 = 7;
t1 = t0 + Dt1;
t2 = t3 - Dt3;
Dt2= t2 - t1;


avVelocity1 = sigfig( Dx1 / Dt1 , 3 );
avVelocity = sigfig( (x3 - x0) / (t3 - t0) , 3 );
avSpeed = sigfig( ( abs(Dx1) + abs(Dx2) + abs(Dx3) ) / (t3 - t0) , 3 );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avVelocity1</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.03]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die mittlere <em>Geschwindigkeit</em> im Zeitintervall \( \left[ {t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \)?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avVelocity</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.03]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die mittlere <em>Geschwindigkeit</em> während der gesamten Bewegung?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avSpeed</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_relerr < 0.03]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist das mittlere <em>Tempo</em> während der gesamten Bewegung?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8629  -->
  <question type="formulas">
    <name>
      <text>Mittlere/momentane Geschwindigkeit und Tempo aus x(t)-Diagramm</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Gegeben sei das folgende \(x(t)\)-Diagramm für die geradlinige, abschnittsweise gleichförmige Bewegung eines Körpers.</p>

<br><jsxgraph width="600" height="300">

JXG.Options.elements.highlight = false;
JXG.Options.point.infoboxDigits = 1;
// JXG.Options.board.keepaspectratio= false;
// JXG.Options.board.axis= false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

const colRed = '#F44336', colPink = '#E91E63', colPurple = '#9C27B0', 
	  colBlue = '#2196F3', colIndigo = '#3F51B5', colCyan = '#00BCD4',
	  colGreen = '#4CAF50', colYellow = '#FFEB3B', colAmber = '#FFC107', 
	  colOrange = '#FF9800', colBrown = '#795548', colGrey = '#9E9E9E';


//-- Set global parameters for before and after submission


const element = document.getElementById("page");
const isMoodle = (typeof(element) != 'undefined' && element != null);

const X = isMoodle ? [{t0},{t1},{t2},{t3}] : [0,2.1,4.5,9];
const Y = isMoodle ? [{x0},{x1},{x2},{x3}] : [15,25,-10,0];
    

//-- Create boards and axes

const brdAttr = { boundingbox: [-1, 35, 10, -35], axis:true,
	defaultAxes: {
		x: {withLabel: true, name: 't in s',
			label: {position: 'rt', offset: [-0, 15], anchorX: 'right'} },
		y: {withLabel:true, name: 'x in m', 
			ticks: {ticksDistance: 5, insertTicks: false, minorTicks: 0},
			label: {position: 'rt', offset: [+15, -0]} } 
		}
	};

var brd0;
if (isMoodle) {
	brd0 = JXG.JSXGraph.initBoard(BOARDID, brdAttr);
} else {
	brd0 = JXG.JSXGraph.initBoard('jxgbox', brdAttr);
	console.log(brd0);
}

	 
// var xAxis = brd0.create('axis', [ [-21,0],[21,0] ], {
// 	straightFirst: false, straightLast: false,
// 	withLabel: true, name: 't in s', /* name: '$$x\\;\\mathrm{(m)}$$', */
// 	label: {position: 'rt', offset: [0, 15], anchorX: 'right', parse: true, fontSize: 12 },
// 	ticks: {ticksDistance: 10,  majorHeight: 10, tickEndings: [0,1],
// 		insertTicks: false, minorTicks: 1, minorHeight: ()=>10, drawZero: true,
// 		label: {visible: true, anchorX: 'middle', anchorY: 'top', offset: [0,-7] } }
// 	});
// var yAxis = brd0.create('axis', [ [0,0],[0,1.2] ], {
// 	straightFirst: false, straightLast: false,
// 	withLabel: true, name: 'x in m', /* name: '$$I/I_0$$', */
// 	label: {position: 'rt', offset: [12, -3], anchorX: 'left', parse: true, fontSize: 12 },
// 	ticks: {ticksDistance: 1, majorHeight: ()=>9*2*brd0.unitX, tickEndings: [0,0],
// 		insertTicks: false, minorTicks: 0, minorHeight: ()=>9*2*brd0.unitX, drawZero: true,
// 		label: {visible: false, anchorX: 'right', anchorY: 'middle', offset: [-3,0] } }
// 	}); 



//-- Objects in diagram


var p = [];

for (let j = 0; j != X.length; j++) {
	p.push(brd0.create('point', [X[j], Y[j]], {fixed: true, withLabel: false,
		fillColor: colBlue, strokeWidth: 0, size: 2 } ));
}

brd0.create('polygonalchain', p, {borders: {strokeColor: colBlue, strokeWidth: 2 }});
// brd0.create('spline', p, { strokeColor: colBlue, strokeWidth: 2 });


</jsxgraph><br>

<p>Geben Sie die gesuchten Grössen mit 1 Nachkommastelle (in m/s) und Einheit an.</p><br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>4.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>arrPositions = shuffle([-30:31:3]);
arrTimes = shuffle([1.2,1.4,1.6,1.8,2.2,2.4,2.6,2.8,3.2,3.4,3.6,3.8]);</text>
</varsrandom>
<varsglobal><text>x0 = arrPositions[0];
x1 = arrPositions[1];
x2 = arrPositions[2];
x3 = arrPositions[3];

Dx1 = x1 - x0;
Dx2 = x2 - x1;
Dx3 = x3 - x2;

Dt1 = arrTimes[0];
Dt3 = arrTimes[1];
t0 = 0;
t3 = 9;
t1 = t0 + Dt1;
t2 = t3 - Dt3;
Dt2= t2 - t1;

t23 = round( (t2+t3) / 2 , 1 );

avVelocity1 = round( Dx1 / Dt1 , 1 );
avVelocity = round( (x3 - x0) / (t3 - t0) , 1 );
avSpeed = round( ( abs(Dx1) + abs(Dx2) + abs(Dx3) ) / (t3 - t0) , 1 );
momVelocity23 = round( Dx3 / Dt3 , 1 );</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avVelocity1</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die mittlere <em>Geschwindigkeit</em> im Zeitintervall \( \left[ {t0}\,\mathrm{s}; {t1}\,\mathrm{s} \right] \)?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text>\[ \overline{v_x}
	= \frac{x_1-x_0}{t_1-t_0}
	= \frac{({x1}\,\mathrm{m})-({x0}\,\mathrm{m})}{{t1}\,\mathrm{s}-{t0}\,\mathrm{s}}
	= {avVelocity1}\,\mathrm{m/s} \]</text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>1</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avVelocity</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die <em>mittlere Geschwindigkeit</em> während der gesamten Bewegung?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text>\[ \overline{v_x}
	= \frac{x_3-x_0}{t_3-t_0}
	= \frac{({x3}\,\mathrm{m})-({x0}\,\mathrm{m})}{{t3}\,\mathrm{s}-{t0}\,\mathrm{s}}
	= {avVelocity}\,\mathrm{m/s} \]</text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>2</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>avSpeed</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist das <em>mittlere Tempo</em> während der gesamten Bewegung?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text>\[ \overline{v}
	= \frac{\left|x_1-x_0\right|+\left|x_2-x_1\right|+\left|x_3-x_2\right|}{t_3-t_0}
	= \frac{\left|{Dx1}\,\mathrm{m}\right|+\left|{Dx2}\,\mathrm{m}\right|+\left|{Dx3}\,\mathrm{m}\right|}{{t3}\,\mathrm{s}-{t0}\,\mathrm{s}}
	= {avSpeed}\,\mathrm{m/s} \]</text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
<answers>
 <partindex>
  <text>3</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>1</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>momVelocity23</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text><![CDATA[_err < 0.1]]></text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text>m/s</text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="markdown">
<text><![CDATA[<p>Was ist die  <em>momentane Geschwindigkeit </em> zum Zeitpunkt \(t={t23}\,\mathrm{s} \)?</p>
<p>&emsp;{_0}{_u}</p>]]></text>
 </subqtext>
 <feedback format="html">
<text>\[ v_x
	= \frac{x_3-x_2}{t_3-t_2}
	= \frac{({x3}\,\mathrm{m})-({x2}\,\mathrm{m})}{{t3}\,\mathrm{s}-{t2}\,\mathrm{s}}
	= {momVelocity23}\,\mathrm{m/s} \]</text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8630  -->
  <question type="formulas">
    <name>
      <text>Momentangeschwindigkeiten bestimmen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Die blaue Kurve im \(x(t)\)-Diagramm zeigt die Bewegung eines Körpers.</p>
<p>Die drei Punkte (blau, rot, grün) und damit die rote Sekante und grüne Tangente können Sie als Werkzeuge nutzen. Sie gehen nicht in die Bewertung ein.</p>

<div><jsxgraph width="500" height="400">

JXG.Options.elements.highlight = false;
JXG.Options.infobox.fontSize = 13;
JXG.Options.infobox.strokeColor = '#000';
JXG.Options.board.showCopyright= false;
JXG.Options.board.showNavigation= false;
JXG.Options.board.zoom= {enabled: false, wheel: false};
JXG.Options.board.pan= {enabled: false, needTwoFingers: false};

  JXG.Options.point.infoboxDigits = 1;
  JXG.Options.point.snapToGrid = true;
  JXG.Options.point.snapSizeX = 0.1;
  JXG.Options.point.snapSizeY = 0.001;

  // Attributes for points and lines

  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';

  var brd = JXG.JSXGraph.initBoard(BOARDID, {
    boundingbox: [-1, 22, 7, -22], axis:true,
    defaultAxes: {
      x: {withLabel: true, name: '$$t\\;\\mathrm{(s)}$$',
          label: {position: 'rt', offset: [10, 26], anchorX: 'right', parse: false, fontSize: 12 } },
      y: {withLabel:true, name: '$$x\\;\\mathrm{(m)}$$',
          label: {position: 'rt', offset: [10, 15], parse: false, fontSize: 12 } } }
    });
  // console.log(brd);

  // Define elements on brd0
  function xFun(t){ return {X0val} + {V0val}*t + 0.5*{A0val}*t*t; }
  function vFun(t){ return {V0val} + {A0val}*t; }
  function tFun(t, tP){ return vFun(tP) * (t - tP) + xFun(tP); }

  const func = brd.create('functiongraph', [function(t){ return xFun(t) }, 0, 10], 
               {strokeColor:col1, strokeWidth:3 });
  
  const pT = brd.create('point', [2, tFun(2,1)], {withLabel:false, size: 2, color:col3 });   // Point on tangent
  const pG = brd.create('point', [4, xFun(4)], {withLabel:false, size: 2, color:col2 });    // Glider for secant
  const pP = brd.create('point', [1, xFun(1)], {withLabel:false, size: 2, color:col1 });      // Glider on function
  const pD = brd.create('point', [8, tFun(8,1)], {visible: false });                                       // Dummy for tangent

  pP.on('drag', function() { this.moveTo([this.X(), xFun(this.X())], 0); });
  pP.on('drag', function() { pT.moveTo([pT.X(), tFun(pT.X(), this.X())], 0); });
  pP.on('drag', function() { pD.moveTo([pD.X(), tFun(pD.X(), this.X())], 0); });
  pG.on('drag', function() { this.moveTo([this.X(), xFun(this.X())], 0); });
  pT.on('drag', function() { this.moveTo([this.X(), tFun(this.X(), pP.X())], 0); });
 
  brd.create('line', [pP,pG], {fixed: true, strokeColor:col2, strokeWidth:2 });                // Secant
  brd.create('line', [pP,pD], {fixed: true, strokeColor:col3, strokeWidth:2 });                // Tangent

</jsxgraph></div><br>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>3.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>V0sign = {-1,1};
X0abs = {1.0:5.1:0.1};
V0abs = {2.0:4.1:0.1};
A0abs = {1.0:2.4:0.1};

t1 = {2.0:5.1:0.1};</text>
</varsrandom>
<varsglobal><text>X0val = round( +V0sign * X0abs , 1);
V0val = round( +V0sign * V0abs , 1);
A0val = round( -V0sign * A0abs , 1);


v0 = V0val;
v1 = round( V0val + A0val * t1 , 1);
vm = round( (v0 + v1) / 2 , 1);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>3</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>3</text>
 </numbox>
 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>[v0, v1, vm]</text>
 </answer>
 <vars2>
  <text><![CDATA[S_v0 = _0;
S_v1 = _1;
S_vm = _2;

Alt_vm = (S_v0 + S_v1) / 2;

crit_v0 = ( abs( S_v0 - v0 ) < 0.11 );
crit_v1 = ( abs( S_v1 - v1 ) < 0.11 );
crit_vm = ( abs( S_vm - vm ) < 0.11 ) | ( abs( S_vm - Alt_vm ) < 0.11 );

crit_tot = ( crit_v0 + crit_v1 + crit_vm ) / 3;]]></text>
 </vars2>
 <correctness>
  <text>crit_tot</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Was ist die Anfangsgeschwindigkeit der Bewegung (bei  \(t_0={0}\))?</p>
<p>&emsp; \(v_{x,0}=\;\) {_0} \(\mathrm{m/s}\)</p>

<p>Was ist die Momentangeschwindigkeit zum Zeitpunkt \(t_1={t1}\,\mathrm{s}\)?</p>
<p>&emsp; \(v_{x,1}=\;\) {_1} \(\mathrm{m/s}\)</p>

<p>Welche mittlere Geschwindigkeit hatte der Körper in den ersten  \({t1}\,\mathrm{s}\)?</p>
<p>&emsp; \(\overline{v}_x=\;\) {_2} \(\mathrm{m/s}\)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8631  -->
  <question type="formulas">
    <name>
      <text>Wachstum einer Fichte</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Eine wachsende Fichte wurde über einen längeren Zeitraum beobachtet. Zu Beginn war sie \({hA}\,\mathrm{m}\) gross und wuchs fortan mit \({v}\,\mathrm{m}\) pro Jahr.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Die Antwort ist richtig.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Die Antwort ist teilweise richtig.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Die Antwort ist falsch.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>hA = {2.1:4.0:0.2};
v = {4.1:5.6:0.2};
hE= {31:46:2};</text>
</varsrandom>
<varsglobal><text>### Generate numbers

origSol = (hE-hA)/v;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>1</text>
 </numbox>
 <vars1>
  <text>nSigFig = 2;
sol = sigfig( origSol , nSigFig );</text>
 </vars1>
 <answer>
  <text>sol</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns = _0;

# Analyze solution for mantissa, exponent, significant figures
expSol = floor( log10(abs(origSol)) );
mantSol = origSol / ( 10**( expSol ));

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct exponent
critExp= ( abs( expAns - expSol ) < 0.1 );

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
mantSolSFA = sigfig( mantSol , nSigFigAns );
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Calculate grade
critMant = max( critMantCorrect, 0.6*critMantRnd1, 0.3*critMantRnd2 );
grade = ( critMant > 0 ) ? 0.3*critExp + 0.1*critSigFig + 0.6*critMant : 0;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<p>Wie lange dauerte es, bis die Fichte \({hE}.0\,\mathrm{m}\) gross war?</p>
<p>&emsp;{_0}&ensp;Jahre&emsp; (Angabe ohne Einheit)</p>]]></text>
 </subqtext>
 <feedback format="markdown">
<text></text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
  </question>

<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Wirkung mehrerer Kräfte</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8634  -->
  <question type="formulas">
    <name>
      <text>Normalkraft als Vektor markieren</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Eine Kiste liegt auf dem Tisch. Von einer Hand wird eine zusätzliche Kraft \(F_\mathrm{H}\) auf sie ausgeübt. Verändern Sie den dritten Pfeil (welcher ursprünglich rechts liegt) mit Hilfe der roten Punkte so, dass er nach Richtung und Betrag der Normalkraft entspricht.</p>]]></text>
    </questiontext>
    <generalfeedback format="markdown">
      <text></text>
    </generalfeedback>
    <defaultgrade>2.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="markdown">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="markdown">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="markdown">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>FGy = {-4:-1.7:0.2};

FHy = {-2,-1.8,-1.6,-1.4,-1.2,-1.0,-0.8,-0.6,-0.4,0.4,0.6,0.8,1.0,1.2,1.4,1.6,1.8,2.0};</text>
</varsrandom>
<varsglobal><text>FNy = - (FGy + FHy);</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
 </placeholder>
 <answermark>
  <text>2</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>2</text>
 </numbox>
 <vars1>
  <text>sol0 = 0;
sol1 = FNy;</text>
 </vars1>
 <answer>
  <text>[0,FNy]</text>
 </answer>
 <vars2>
  <text><![CDATA[origAns0 = _0;
origAns1 = _1;

crit0Cor = ( abs(origAns0 - sol0) < 0.1 );

crit1Cor = ( abs(origAns1 - sol1) < 0.1 );
crit1Rnd1 = ( abs(origAns1 - sol1) < 0.3 );
crit1Rnd2 = ( abs(origAns1 - sol1) < 0.5 );

# Calculate grade
grade0 = crit0Cor;
grade1 = max( 1.0* crit1Cor, 0.8* crit1Rnd1, 0.6* crit1Rnd2 );
grade = 0.2* grade0 + 0.8* grade1;]]></text>
 </vars2>
 <correctness>
  <text>grade</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
 </unitpenalty>
 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
 </otherrule>
 <subqtext format="html">
<text><![CDATA[<jsxgraph width="500" height="500" numberOfBoards="1" ext_formulas>

// JavaScript code to create the construction.
var jsxCode = function (question) {
  
  JXG.Options.point.showInfobox = false;
  JXG.Options.point.snapSizeX = 0.2;
  JXG.Options.point.snapSizey = 0.2;
  JXG.Options.point.snapToGrid = true;
  JXG.Options.arrow.lastArrow.size = 4;
  JXG.Options.arrow.lineCap = 'round';

  const col1='#4285F4', col2='#EA4335', col3='#34A853', col4='#FBBC05';
  const colVecN='#4285F4', colVecS='#EA4335', colVecC='#34A853';

  // Attributes for points
  function attrPvelV(addAttr={}) {
    const attr = {fixed:true, color:'none', withLabel:false, size:2 };
    return { ...attr, ...addAttr};
  }
  function attrPvelU(addAttr={}) {
    const attr = {fixed:question.isSolved, withLabel:false, size:2 };
    return { ...attr, ...addAttr};
  }
  const attrVecN = {fixed:question.isSolved, strokeColor:colVecN, strokeWidth:3, name:'F_N' };
  

  // Import final coordinates after submission
  var [FNx, FNy] = question.getAllValues([0.4, 0.4]);
  
  const FGy = {FGy}, FHy = {FHy};
  

  // Create boards
  var brd0 = question.initBoard(BOARDID0, { 
    boundingbox:[-5, 5, 5, -5], axis:false, keepaspectratio:true,
    zoom: {enabled:false, wheel: false}, pan: {enabled:false, needTwoFingers: false},
    showCopyright:false, showNavigation:false 
    });


  
  brd0.create('image', ['@@PLUGINFILE@@/fig_Normalkraft.png', [-3,-3], [6,6]], {fixed:true});

  brd0.create('arrow', [ [0,-0.6] , [0,-0.6+FGy] ], {fixed:true, strokeColor:colVecN, strokeWidth:3,
            name:'$$\\vec{F}_\\mathrm{G}$$', withLabel:true, label:{parse:false, position:'rt', offset:[-35,25], color:colVecN, fontSize:16 } } );
  brd0.create('arrow', [ [-0.2,0] , [-0.2,FHy] ], {fixed:true, strokeColor:colVecN, strokeWidth:3,
            name:'$$\\vec{F}_\\mathrm{H}$$', withLabel:true, label:{parse:false, position:'rt', offset:[-35,25], color:colVecN, fontSize:16 } } );

  var pFN_0 = brd0.create('point', [0.2, -1.2], attrPvelU() );
  var pFN_1 = brd0.create('point', [0.2+FNx, -1.2+FNy], attrPvelU() );

  brd0.create('arrow', [ pFN_0, pFN_1 ], attrVecN );



  question.bindInput(0, () => { return ( pFN_1.X()-pFN_0.X() ); });
  question.bindInput(1, () => { return ( pFN_1.Y()-pFN_0.Y() ); });

  };
  
  // Execute the JavaScript code.
  new JSXQuestion(BOARDID0, jsxCode, allowInputEntry=false);
  
</jsxgraph>]]></text>
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 </subqtext>
 <feedback format="markdown">
<text>Ihre Antwort war: {=_0}, {=_1}</text>
 </feedback>
 <correctfeedback format="markdown">
<text></text>
 </correctfeedback>
 <partiallycorrectfeedback format="markdown">
<text></text>
 </partiallycorrectfeedback>
 <incorrectfeedback format="markdown">
<text></text>
 </incorrectfeedback>
</answers>
    <tags>
      <tag><text>JXG</text>
</tag>
    </tags>
  </question>

<!-- question: 8635  -->
  <question type="formulas">
    <name>
      <text>Wirkende Kräfte und Kräftegleichgewicht</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>{Premise}&nbsp;Was folgt daraus?</p>]]></text>
    </questiontext>
    <generalfeedback format="html">
      <text>Ein Körper ist kräftefrei bzw. im Kräftegleichgewicht, wenn er keine Kräfte von anderen Körpern erfährt oder diese sich insgesamt aufheben, sodass die resultierende Kraft Null ist. Ist ein Körper kräftefrei, ändert sich seine Bewegung nicht.</text>
    </generalfeedback>
    <defaultgrade>1.0000000</defaultgrade>
    <penalty>0.3333333</penalty>
    <hidden>0</hidden>
    <idnumber></idnumber>
    <correctfeedback format="html">
      <text>Your answer is correct.</text>
    </correctfeedback>
    <partiallycorrectfeedback format="html">
      <text>Your answer is partially correct.</text>
    </partiallycorrectfeedback>
    <incorrectfeedback format="html">
      <text>Your answer is incorrect.</text>
    </incorrectfeedback>
    <shownumcorrect/>
<varsrandom><text>j = {0:4};</text>
</varsrandom>
<varsglobal><text><![CDATA[Premises = [
	"An einem Körper greifen entlang derselben Wirkungslinie zwei gleich grosse, entgegengesetzt gerichtete Kräfte an.",
	"Auf einen Körper wirkt genau eine Kraft.",
	"An einem Körper greifen zwei gleich grosse Kräfte an.",
	"Ein Körper ist kräftefrei." ];

Conclusions = [
	"Der Körper behält seine Geschwindigkeit bei.",
	"Der Körper wird beschleunigt.",
	"Das kann man nicht sagen." ];

Premise = Premises[j];

ansJ = (j == 3) ? 0 : j ;]]></text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
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  <text></text>
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 <answermark>
  <text>1</text>
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 <answertype>
  <text>0</text>
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 <numbox>
  <text>1</text>
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 <vars1>
  <text></text>
 </vars1>
 <answer>
  <text>ansJ</text>
 </answer>
 <vars2>
  <text></text>
 </vars2>
 <correctness>
  <text>_err == 0</text>
 </correctness>
 <unitpenalty>
  <text>0.2</text>
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 <postunit>
  <text></text>
 </postunit>
 <ruleid>
  <text>1</text>
 </ruleid>
 <otherrule>
  <text></text>
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      <tag><text>1P</text>
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<!-- question: 8636  -->
  <question type="formulas">
    <name>
      <text>Wirkende Kräfte und Kräftegleichgewicht (obsolet)</text>
    </name>
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      <text><![CDATA[<p dir="ltr" style="text-align: left;">{Premise}&nbsp;Was folgt daraus?</p>]]></text>
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<varsrandom><text>j = {0:3};</text>
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<varsglobal><text><![CDATA[Premises = [
	"Ein Körper ist kräftefrei.",
	"An einem Körper greifen entlang derselben Wirkungslinie, aber an unterschiedlichen Orten zwei gleich grosse, entgegengesetzt gerichtete Kräfte an.",
	"Auf einen Körper wirkt genau eine Kraft.",
	"An gegenüberliegenden Seiten eines Körpers greifen zwei gleich grosse Kräfte an." ];

Conclusions = [
	"Der Körper wird nicht verformt und ändert seinen Bewegungszustand nicht.",
	"Der Körper wird verformt, aber behält seinen Bewegungszustand bei.",
	"Der Körper wird beschleunigt.",
	"Das kann man nicht sagen." ];

Premise = Premises[j];]]></text>
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<text><![CDATA[<p dir="ltr" style="text-align: left;">{_0:Conclusions}</p>]]></text>
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    <tags>
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      <tag><text>1P</text>
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<!-- question: 0  -->
  <question type="category">
    <category>
      <text>top/2 Mechanik/Impuls und Impulserhaltung</text>
    </category>
    <info format="moodle_auto_format">
      <text></text>
    </info>
    <idnumber></idnumber>
  </question>

<!-- question: 8641  -->
  <question type="formulas">
    <name>
      <text>Impulserhaltung: Stoss zweier Wagen</text>
    </name>
    <questiontext format="html">
      <text><![CDATA[<p>Auf einer Metallschiene befinden sich zwei leicht laufende Wagen A und B mit Massen \(m_\mathrm{A}\) und \(m_\mathrm{B}\). Wagen B steht zunächst still. Wagen A wird angestossen und prallt mit seiner Anfangsgeschwindigkeit \(\vec{v}_\mathrm{A,1}\) auf Wagen B.</p>

<br><p><img alt="" class="img-responsive" src="@@PLUGINFILE@@/tikz_ElastischerStossVerschiedeneMassen.svg" width="600px"/></p><br>

<p>Berechnen Sie alle in der Tabelle fehlenden Werte.</p>
<p>Hinweise:</p>
<ul>
	<li>Rechnen Sie stets mit ungerundeten Zwischenergebnissen weiter!</li>
	<li>Füllen Sie bitte in jedem Fall alle Felder aus, damit Ihre Punkte berechnet werden können.</li>
</ul>
<br>]]></text>
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    <shownumcorrect/>
<varsrandom><text>mA = {0.101:0.399:0.002};
mB = {0.501:0.799:0.002};

vxA1 = {0.31:0.99:0.02};</text>
</varsrandom>
<varsglobal><text>nSigFig = 2;

vxA2 = sigfig( (mA-mB)/(mA+mB) * vxA1 , nSigFig);

pxA1 = mA * vxA1;
pxA2 = mA * vxA2;
pxB2 = pxA1 - pxA2;
vxB2 = pxB2 / mB;</text>
</varsglobal>
<answernumbering><text>none</text>
</answernumbering>
<answers>
 <partindex>
  <text>0</text>
 </partindex>
 <placeholder>
  <text></text>
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 <answermark>
  <text>5</text>
 </answermark>
 <answertype>
  <text>0</text>
 </answertype>
 <numbox>
  <text>4</text>
 </numbox>
 <vars1>
  <text>origSol0 = pxA1;
sol0 = sigfig( origSol0 , nSigFig );

origSol1 = pxA2;
sol1 = sigfig( origSol1 , nSigFig );

origSol2 = pxB2;
sol2 = sigfig( origSol2 , nSigFig );

origSol3 = vxB2;
sol3 = sigfig( origSol3 , nSigFig );</text>
 </vars1>
 <answer>
  <text>[sol0, sol1, sol2, sol3]</text>
 </answer>
 <vars2>
  <text><![CDATA[##### Answer 0 #####

origAns = _0;
origSol = origSol0;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
nSigFigAns = max( nSigFigAns , 2 );
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade0 = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;


##### Answer 1 #####

origAns = _1;
origSol = origSol1;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
nSigFigAns = max( nSigFigAns , 2 );
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade1 = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;


##### Answer 2 #####

origAns = _2;
origSol = _0 - _1;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
nSigFigAns = max( nSigFigAns , 2 );
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade2 = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;


##### Answer 3 #####

origAns = _3;
origSol = _2 / mB;

# Analyze answer for mantissa, exponent, significant figures
expAns = floor( log10(abs(origAns)));
mantAns = origAns / ( 10**( expAns ));
nSigFigAns = 1;
for (i:[0:6]) {
    shiftedNum = round( 10**(i) * abs(mantAns) , 6 );
    divTest = round( shiftedNum - floor(shiftedNum) , 6);
    nSigFigAns = ( divTest > 1e-5 ) ? nSigFigAns+1 : nSigFigAns ;
}

# Correct number of significant figures
critSigFig = ( abs( nSigFigAns - nSigFig ) < 1.1 );

# Correct mantissa with respect to significant figures
nSigFigAns = max( nSigFigAns , 2 );
origSolSFA = sigfig( origSol , nSigFigAns );
expSolSFA = floor( log10(abs(origSolSFA)) );
mantSolSFA = origSolSFA / ( 10**( expSolSFA ));
critMantCorrect = ( abs( mantAns - mantSolSFA ) < 0.5 * 0.1**(nSigFigAns-1) );

# Mantissa with rounding errors with respect to significant figures
critMantRnd1 = ( abs( mantAns - mantSolSFA ) < 1.5 * 0.1**(nSigFigAns-1) );
critMantRnd2 = ( abs( mantAns - mantSolSFA ) < 2.5 * 0.1**(nSigFigAns-1) );

# Correct exponent
critExp= ( abs( expAns - expSolSFA ) < 0.1 );

# Calculate grade
critMant = max( critMantCorrect, 0.8*critMantRnd1, 0.6*critMantRnd2 );
grade3 = ( critMant > 0 ) ? 0.2*critExp + 0.1*critSigFig + 0.7*critMant : 0;



##### Final grade #####

grade = 0.2*grade0 + 0.2*grade1 + 0.3*grade2 + 0.3*grade3;]]></text>
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