https://learnlab.org/wiki/index.php?title=Macro-level_framework&feed=atom&action=historyMacro-level framework - Revision history2024-03-29T01:18:20ZRevision history for this page on the wikiMediaWiki 1.31.12https://learnlab.org/wiki/index.php?title=Macro-level_framework&diff=4752&oldid=prevVanlehn at 02:27, 13 April 20072007-04-13T02:27:22Z<p></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 02:27, 13 April 2007</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== A multi-dimensional framework for the macro level ==  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== A multi-dimensional framework for the macro level ==  </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">[This has not been vetted by Ken, Chuck or the EC.  Consider it a personal opinion.  -- Kurt]</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Although many classification schemes may make sense for the macro level (PSLC’s [[Root node | cluster-subcluster hierarchy]] is one), this page presents a multidimensional one.  That is, this classification scheme defines a set of instructional dimensions, where each dimension has a set of alternative, non-numeric values.  A point in this multidimensional space is simply a specification of a value for each dimension.  Each point in the space corresponds to a generic type of instruction.  [[Instructional dimensions root | Click here for a list of the instructional dimensions currently being investigated.]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Although many classification schemes may make sense for the macro level (PSLC’s [[Root node | cluster-subcluster hierarchy]] is one), this page presents a multidimensional one.  That is, this classification scheme defines a set of instructional dimensions, where each dimension has a set of alternative, non-numeric values.  A point in this multidimensional space is simply a specification of a value for each dimension.  Each point in the space corresponds to a generic type of instruction.  [[Instructional dimensions root | Click here for a list of the instructional dimensions currently being investigated.]]</div></td></tr>
</table>Vanlehnhttps://learnlab.org/wiki/index.php?title=Macro-level_framework&diff=4716&oldid=prevVanlehn: /* Draft version of a multi-dimensional framework for the Macro Level */2007-04-07T18:52:14Z<p><span dir="auto"><span class="autocomment">Draft version of a multi-dimensional framework for the Macro Level</span></span></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 18:52, 7 April 2007</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">=== Draft version </del>of a <del class="diffchange diffchange-inline">multi-dimensional framework for </del>the <del class="diffchange diffchange-inline">Macro Level ===</del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">The overarching research goal </ins>of <ins class="diffchange diffchange-inline">the PSLC is to understand [[robust learning]], and in particular, to delineate both the conditions under which robust learning occurs and the mechanisms that underlie it.  Roughly aligned with these questions are our two major level of explanation.  The [[macro level]], which focuses mostly on identifying the conditions where robust learning occurs.  It is based on observable conditions, activities and results.  The [[micro level]], which focuses mostly on identifying the mechanisms that underlie robust learning.  It is based on inference of unobservable conditions, activities and results. </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Just as medicine finds indispensable both the clinical and biological levels of explanation, the PSLC has found both its levels to be indispensable as well.  The macro level is like the clinical level—it is </ins>a <ins class="diffchange diffchange-inline">relatively atheoretical classification of treatments and effects.  The micro level is like </ins>the <ins class="diffchange diffchange-inline">biological level—it explains why certain treatments have certain effects. </ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">The overarching research goal of </del>the <del class="diffchange diffchange-inline">PSLC is to understand [[robust learning]], and in particular, to delineate both the conditions under which robust learning occurs and the mechanisms that underlie it.  Roughly aligned with these questions are our two major level of explanation.  The [[</del>macro level<del class="diffchange diffchange-inline">]], which focuses mostly on identifying the conditions where robust learning occurs.  It is based on observable conditions, activities and results.  The [[micro level]], which focuses mostly on identifying the mechanisms that underlie robust learning.  It is based on inference of unobservable conditions, activities and results. </del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">== A multi-dimensional framework for </ins>the macro level <ins class="diffchange diffchange-inline">== </ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">Just as medicine finds indispensable both </del>the <del class="diffchange diffchange-inline">clinical and biological levels </del>of <del class="diffchange diffchange-inline">explanation</del>, <del class="diffchange diffchange-inline">the PSLC </del>has <del class="diffchange diffchange-inline">found both its levels to be indispensable as well</del>.  <del class="diffchange diffchange-inline">The macro level </del>is <del class="diffchange diffchange-inline">like </del>the <del class="diffchange diffchange-inline">clinical level—it is </del>a <del class="diffchange diffchange-inline">relatively atheoretical classification </del>of <del class="diffchange diffchange-inline">treatments and effects</del>. <del class="diffchange diffchange-inline"> The micro level is like </del>the <del class="diffchange diffchange-inline">biological level—it explains why certain treatments have certain effects</del>. <del class="diffchange diffchange-inline"> </del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Although many classification schemes may make sense for </ins>the <ins class="diffchange diffchange-inline">macro level (PSLC’s [[Root node | cluster-subcluster hierarchy]] is one), this page presents a multidimensional one.  That is, this classification scheme defines a set </ins>of <ins class="diffchange diffchange-inline">instructional dimensions</ins>, <ins class="diffchange diffchange-inline">where each dimension </ins>has <ins class="diffchange diffchange-inline">a set of alternative, non-numeric values</ins>.  <ins class="diffchange diffchange-inline">A point in this multidimensional space </ins>is <ins class="diffchange diffchange-inline">simply a specification of a value for each dimension.  Each point in </ins>the <ins class="diffchange diffchange-inline">space corresponds to </ins>a <ins class="diffchange diffchange-inline">generic type </ins>of <ins class="diffchange diffchange-inline">instruction</ins>. <ins class="diffchange diffchange-inline">  [[Instructional dimensions root | Click here for a list of </ins>the <ins class="diffchange diffchange-inline">instructional dimensions currently being investigated</ins>.<ins class="diffchange diffchange-inline">]]</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">Although many classification schemes may make sense for the macro level (PSLC’s cluster-subcluster hierarchy is one), this page presents a multidimensional one.  That is, this classification scheme defines a set of design dimension, where each dimension has a set of alternative, non-numeric values.  A point in this multidimensional space is simply a specification of a value for each dimension.  Each point in the space corresponds to a generic type of instruction.  </del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Whenever possible, the values of <del class="diffchange diffchange-inline">a </del>dimension are ordered or partially ordered by the amount of assistance they offer students, where higher assistance values raise performance during training.  For instance, along the feedback timing dimension, offering immediate feedback increases performance during training (measured by uncorrected errors and time-to-completion) compared to delayed feedback, so immediate feedback is a higher assistance value for the feedback-timing dimension than delayed feedback.  This ordering allows testing the PSLC’s Assistance Hypothesis:</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Whenever possible, the values of <ins class="diffchange diffchange-inline">an instructional </ins>dimension are ordered or partially ordered by the amount of <ins class="diffchange diffchange-inline">[[</ins>assistance<ins class="diffchange diffchange-inline">]] </ins>they offer students, where higher assistance values raise performance during training.  For instance, along the feedback timing dimension, offering immediate feedback increases performance during training (measured by uncorrected errors and time-to-completion) compared to delayed feedback, so immediate feedback is a higher assistance value for the feedback-timing dimension than delayed feedback.  This ordering allows testing the PSLC’s <ins class="diffchange diffchange-inline">[[</ins>Assistance Hypothesis<ins class="diffchange diffchange-inline">]]</ins>:</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Experiments are used to map out the multidimensional design space.  A typical experiment compares two points (instructional treatments) that have different values on one <del class="diffchange diffchange-inline">dimesion </del>and the same values along all <del class="diffchange diffchange-inline">others</del>.  If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately.  Prior knowledge is measured by pretesting or its LearnLab equivalent.  Immediate and robust learning is assessed with the usual LearnLab measures.  Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of the Assistance Hypothesis.  With hundreds of such experiments, we should be able to understand <del class="diffchange diffchange-inline">a design </del>space with thousands or millions of points.   </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Experiments are used to map out the multidimensional design space.  A typical experiment compares two points (instructional treatments) that have different values on one <ins class="diffchange diffchange-inline">dimension </ins>and the same values along all <ins class="diffchange diffchange-inline">other dimensions</ins>.  If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately.  Prior knowledge is measured by pretesting or its LearnLab equivalent.  Immediate and robust learning is assessed with the usual LearnLab measures.  Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of the Assistance Hypothesis.  With hundreds of such experiments, we should be able to understand <ins class="diffchange diffchange-inline">an instructional </ins>space with thousands or millions of points.   </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Note that such an understanding would be only a macro level one (like the clinical level in medicine).  A micro level understanding (like the biological level) is related, but it describes describes cognitive, motivational, social and other processes, and it distinguishes the learners' choices from the affordances/constraints of the instruction.  Several micro-level explanations may be consistent with a single macro-level results, so experiments may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to determine which micro-level explanations is more accurate.</ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">Note that such an understanding would be only a macro level one (like the clinical level in medicine).  A micro level understanding (like the biological level) is related but different in that it describes cognitive, motivational, social and other processes, and it distinguishes the learners' choices from the affordances/constraints of the instruction.  An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.</del></div></td><td colspan="2"> </td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>[[Instructional dimensions root|Click here for a list of the <ins class="diffchange diffchange-inline">instructional </ins>dimensions currently being investigated.]]</div></td></tr>
</table>Vanlehnhttps://learnlab.org/wiki/index.php?title=Macro-level_framework&diff=4694&oldid=prevVanlehn: /* Draft version of a multi-dimensional framework for the Macro Level */2007-04-07T16:39:17Z<p><span dir="auto"><span class="autocomment">Draft version of a multi-dimensional framework for the Macro Level</span></span></p>
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<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 16:39, 7 April 2007</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l14" >Line 14:</td>
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<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Experiments are used to map out the multidimensional design space.  A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others.  If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately.  Prior knowledge is measured by pretesting or its LearnLab equivalent.  Immediate and robust learning is assessed with the usual LearnLab measures.  Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of the Assistance Hypothesis.  With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.   </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Experiments are used to map out the multidimensional design space.  A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others.  If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately.  Prior knowledge is measured by pretesting or its LearnLab equivalent.  Immediate and robust learning is assessed with the usual LearnLab measures.  Such an experiment determines how two independent variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of the Assistance Hypothesis.  With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.   </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Note that such an understanding would be only a macro level one (like the clinical level in medicine).  A micro level understanding (like the biological level) is related but different.  An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Note that such an understanding would be only a macro level one (like the clinical level in medicine).  A micro level understanding (like the biological level) is related but different <ins class="diffchange diffchange-inline">in that it describes cognitive, motivational, social and other processes, and it distinguishes the learners' choices from the affordances/constraints of the instruction</ins>.  An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</div></td></tr>
</table>Vanlehnhttps://learnlab.org/wiki/index.php?title=Macro-level_framework&diff=4692&oldid=prevVanlehn: /* Draft version of a multi-dimensional framework for the Macro Level */2007-04-07T16:34:17Z<p><span dir="auto"><span class="autocomment">Draft version of a multi-dimensional framework for the Macro Level</span></span></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<col class="diff-content" />
<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 16:34, 7 April 2007</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l12" >Line 12:</td>
<td colspan="2" class="diff-lineno">Line 12:</td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.  </div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>* Robust learning will be enhanced by providing assistance is inverse proportion to how well a student knows a component of knowledge.  </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>Experiments are used to map out the multidimensional design space.  A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others.  If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately.  Prior knowledge is measured by pretesting or its LearnLab equivalent.  Immediate and robust learning is assessed with the usual LearnLab measures.  Such an experiment determines how two <del class="diffchange diffchange-inline">indepentent </del>variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of the Assistance Hypothesis.  With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.   </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>Experiments are used to map out the multidimensional design space.  A typical experiment compares two points (instructional treatments) that have different values on one dimesion and the same values along all others.  If the assistance ordering of the compared values is not yet known, it is measured by instrumenting the training appropriately.  Prior knowledge is measured by pretesting or its LearnLab equivalent.  Immediate and robust learning is assessed with the usual LearnLab measures.  Such an experiment determines how two <ins class="diffchange diffchange-inline">independent </ins>variables (prior knowledge and instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of the Assistance Hypothesis.  With hundreds of such experiments, we should be able to understand a design space with thousands or millions of points.   </div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Note that such an understanding would be only a macro level one (like the clinical level in medicine).  A micro level understanding (like the biological level) is related but different.  An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>Note that such an understanding would be only a macro level one (like the clinical level in medicine).  A micro level understanding (like the biological level) is related but different.  An experiment may need to collect finer grained data (e.g., learning curves for individual knowledge components; verbal protocols) in order to test different micro-level explanations of its macro-level results.</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</div></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</div></td></tr>
</table>Vanlehnhttps://learnlab.org/wiki/index.php?title=Macro-level_framework&diff=4691&oldid=prevVanlehn: /* Draft version of a framework for the Macro Level */2007-04-07T16:25:39Z<p><span dir="auto"><span class="autocomment">Draft version of a framework for the Macro Level</span></span></p>
<table class="diff diff-contentalign-left" data-mw="interface">
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<tr class="diff-title" lang="en">
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">← Older revision</td>
<td colspan="2" style="background-color: #fff; color: #222; text-align: center;">Revision as of 16:25, 7 April 2007</td>
</tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td>
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<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>=== Draft version of a framework for the Macro Level ===</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>=== Draft version of a <ins class="diffchange diffchange-inline">multi-dimensional </ins>framework for the Macro Level ===</div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The macro level <del class="diffchange diffchange-inline">framework </del>is based on <del class="diffchange diffchange-inline">a three-part hypothesis</del>.   </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The <ins class="diffchange diffchange-inline">overarching research goal of the PSLC is to understand [[robust learning]], and in particular, to delineate both the conditions under which robust learning occurs and the mechanisms that underlie it.  Roughly aligned with these questions are our two major level of explanation.  The [[</ins>macro level<ins class="diffchange diffchange-inline">]], which focuses mostly on identifying the conditions where robust learning occurs.  It </ins>is based on <ins class="diffchange diffchange-inline">observable conditions, activities and results</ins>.  <ins class="diffchange diffchange-inline">The [[micro level]], which focuses mostly on identifying the mechanisms that underlie robust learning.  It is based on inference of unobservable conditions, activities and results. </ins></div></td></tr>
<tr><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f8f9fa; color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"># For any instructional </del>design <del class="diffchange diffchange-inline">issue</del>, the <del class="diffchange diffchange-inline">alternative designs can be rank </del>ordered <del class="diffchange diffchange-inline">according to how much </del>assistance they offer the <del class="diffchange diffchange-inline">learner</del>.</div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Just as medicine finds indispensable both the clinical and biological levels of explanation, the PSLC has found both its levels to be indispensable as well.  The macro level is like the clinical level—it is a relatively atheoretical classification of treatments and effects.  The micro level is like the biological level—it explains why certain treatments have certain effects.  </ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"># Learners can also </del>be <del class="diffchange diffchange-inline">rank ordered </del>by how <del class="diffchange diffchange-inline">far along they are in their acquisition </del>of <del class="diffchange diffchange-inline">the </del>knowledge <del class="diffchange diffchange-inline">addressed by the instruction</del>.  </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline"># Learning is most efficient when </del>the <del class="diffchange diffchange-inline">two ranks match inversely</del>.  (<del class="diffchange diffchange-inline">Efficiency is usually defined as learning gains divided by </del>instructional <del class="diffchange diffchange-inline">time</del>.<del class="diffchange diffchange-inline">)</del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Although many classification schemes may make sense for the macro level (PSLC’s cluster-subcluster hierarchy is one), this page presents a multidimensional one.  That is, this classification scheme defines a set of </ins>design <ins class="diffchange diffchange-inline">dimension, where each dimension has a set of alternative</ins>, <ins class="diffchange diffchange-inline">non-numeric values.  A point in this multidimensional space is simply a specification of a value for each dimension.  Each point in </ins>the <ins class="diffchange diffchange-inline">space corresponds to a generic type of instruction.  </ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>   </div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">[[Instructional dimensions root|Click here for a list of the dimensions currently being investigated.]]</ins></div></td></tr>
<tr><td class='diff-marker'>−</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del class="diffchange diffchange-inline">That is, for beginning learners, high </del>assistance <del class="diffchange diffchange-inline">instruction </del>is <del class="diffchange diffchange-inline">more efficient than low assistance instruction</del>, <del class="diffchange diffchange-inline">and for advanced learners, low assistance instruction </del>is <del class="diffchange diffchange-inline">more efficient than high assistance instruction</del>.  <del class="diffchange diffchange-inline">This hypothesis </del>is <del class="diffchange diffchange-inline">familiar to teachers, </del>and <del class="diffchange diffchange-inline">it </del>is <del class="diffchange diffchange-inline">often associated </del>with <del class="diffchange diffchange-inline">Vygotsky </del>and the <del class="diffchange diffchange-inline">motto “model</del>, <del class="diffchange diffchange-inline">scaffold, fade</del>.<del class="diffchange diffchange-inline">” </del>  <del class="diffchange diffchange-inline">Woods, Bruner and Ross [ref] coined </del>the <del class="diffchange diffchange-inline">term “scaffolding” for assistance that </del>is <del class="diffchange diffchange-inline">eventually removed </del>(<del class="diffchange diffchange-inline">faded</del>)<del class="diffchange diffchange-inline">, but the term “scaffolding” is often mystifying </del>to <del class="diffchange diffchange-inline">those outside </del>the <del class="diffchange diffchange-inline">Learning Sciences, so we prefer “assistance</del>.<del class="diffchange diffchange-inline">”</del></div></td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Whenever possible, the values of a dimension are ordered or partially </ins>ordered <ins class="diffchange diffchange-inline">by the amount of </ins>assistance they offer <ins class="diffchange diffchange-inline">students, where higher assistance values raise performance during training.  For instance, along </ins>the <ins class="diffchange diffchange-inline">feedback timing dimension, offering immediate feedback increases performance during training (measured by uncorrected errors and time-to-completion) compared to delayed feedback, so immediate feedback is a higher assistance value for the feedback-timing dimension than delayed feedback</ins>. <ins class="diffchange diffchange-inline">  This ordering allows testing the PSLC’s Assistance Hypothesis:</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">* Robust learning will </ins>be <ins class="diffchange diffchange-inline">enhanced </ins>by <ins class="diffchange diffchange-inline">providing assistance is inverse proportion to </ins>how <ins class="diffchange diffchange-inline">well a student knows a component </ins>of knowledge.  </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Experiments are used to map out </ins>the <ins class="diffchange diffchange-inline">multidimensional design space</ins>.  <ins class="diffchange diffchange-inline">A typical experiment compares two points </ins>(instructional <ins class="diffchange diffchange-inline">treatments) that have different values on one dimesion and the same values along all others</ins>.  <ins class="diffchange diffchange-inline">If the </ins>assistance <ins class="diffchange diffchange-inline">ordering of the compared values </ins>is <ins class="diffchange diffchange-inline">not yet known</ins>, <ins class="diffchange diffchange-inline">it </ins>is <ins class="diffchange diffchange-inline">measured by instrumenting the training appropriately</ins>.  <ins class="diffchange diffchange-inline">Prior knowledge </ins>is <ins class="diffchange diffchange-inline">measured by pretesting or its LearnLab equivalent.  Immediate </ins>and <ins class="diffchange diffchange-inline">robust learning </ins>is <ins class="diffchange diffchange-inline">assessed </ins>with <ins class="diffchange diffchange-inline">the usual LearnLab measures.  Such an experiment determines how two indepentent variables (prior knowledge </ins>and <ins class="diffchange diffchange-inline">instructional assistance) affect two dependent variables (immediate and robust learning).  Such an experiment provides a test of a specific application of </ins>the <ins class="diffchange diffchange-inline">Assistance Hypothesis.  With hundreds of such experiments</ins>, <ins class="diffchange diffchange-inline">we should be able to understand a design space with thousands or millions of points.  </ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">Note that such an understanding would be only a macro level one (like the clinical level in medicine)</ins>.  <ins class="diffchange diffchange-inline">A micro level understanding (like </ins>the <ins class="diffchange diffchange-inline">biological level) </ins>is <ins class="diffchange diffchange-inline">related but different.  An experiment may need to collect finer grained data </ins>(<ins class="diffchange diffchange-inline">e.g., learning curves for individual knowledge components; verbal protocols</ins>) <ins class="diffchange diffchange-inline">in order </ins>to <ins class="diffchange diffchange-inline">test different micro-level explanations of its macro-level results.</ins></div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div> </div></td></tr>
<tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color: #222; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins class="diffchange diffchange-inline">[[Instructional dimensions root|Click here for a list of </ins>the <ins class="diffchange diffchange-inline">dimensions currently being investigated</ins>.<ins class="diffchange diffchange-inline">]]</ins></div></td></tr>
</table>Vanlehnhttps://learnlab.org/wiki/index.php?title=Macro-level_framework&diff=4335&oldid=prevVanlehn at 15:48, 30 March 20072007-03-30T15:48:30Z<p></p>
<p><b>New page</b></p><div>=== Draft version of a framework for the Macro Level ===<br />
<br />
The macro level framework is based on a three-part hypothesis. <br />
<br />
# For any instructional design issue, the alternative designs can be rank ordered according to how much assistance they offer the learner.<br />
# Learners can also be rank ordered by how far along they are in their acquisition of the knowledge addressed by the instruction. <br />
# Learning is most efficient when the two ranks match inversely. (Efficiency is usually defined as learning gains divided by instructional time.)<br />
<br />
That is, for beginning learners, high assistance instruction is more efficient than low assistance instruction, and for advanced learners, low assistance instruction is more efficient than high assistance instruction. This hypothesis is familiar to teachers, and it is often associated with Vygotsky and the motto “model, scaffold, fade.” Woods, Bruner and Ross [ref] coined the term “scaffolding” for assistance that is eventually removed (faded), but the term “scaffolding” is often mystifying to those outside the Learning Sciences, so we prefer “assistance.”</div>Vanlehn