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| − | <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
| + | == REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007) == |
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| + | |
| − | <head>
| + | |
| − | <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
| + | === Logistical Information === |
| − | <meta name="keywords" content="REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)" />
| + | |
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| − | <title> REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)- Pslc</title>
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| + | | '''Contributors''' || Christine M. Feeney and Michael Heilman |
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| + | | '''Study Start Date''' || June, 2007 |
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| + | | '''Study End Date''' || July, 2007 |
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| + | | '''Number of Students''' || 30 |
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| + | | '''Total Participant Hours (est.)''' || 300 |
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| + | | '''Data in Datashop''' || no |
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| + | |} |
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| − | <!-- Head Scripts -->
| + | === Abstract === |
| − | </head>
| + | |
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| + | Previous psychological research has identified two types of comprehension: shallow, in which people can reproduce information, and deep, in which people can comprehend the meaning of information. In addition, researchers have found that these two types of comprehension are separate and disparate. The PSLC currently employs an English as a Second Language vocabulary tutor, REAP, that uses shallow, automatically generated, comprehension questions to check that students are actively reading, rather than just skimming, practice reading passages. The purpose of the current study was to examine performance on the REAP-type questions with manually authored, deeper, reading comprehension questions. Participants were thirty undergraduate students (male = 11) participating in summer research programs at Carnegie Mellon University. The researcher predicted a positive correlation between the two testing types, which was supported by the data (r = .366, p < 0.0005, one-tailed t-test). |
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| + | === Glossary === |
| − | <div id="content">
| + | |
| − | <a name="top" id="top"></a>
| + | ''Shallow Comprehension:'' Processing directed towards reproducing the learning material without necessarily understanding it. |
| − | <h1 class="firstHeading"> REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)</h1>
| + | |
| − | <div id="bodyContent">
| + | ''Deep Comprehension:'' Processing directed towards comprehending the intended meaning of the learning material. |
| − | <h3 id="siteSub">From Pslc</h3>
| + | |
| − | <div id="contentSub"></div>
| + | === Research question === |
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| + | |
| − | <table id="toc" class="toc" summary="Contents"><tr><td><div id="toctitle"><h2>Contents</h2></div>
| + | Does performance on the shallow, automatically-generated questions correlate with performance on more sophisticated, deep reading comprehension questions? |
| − | <ul>
| + | |
| − | <li class='toclevel-1'><a href="#REAP_Study_on_Question_Types"><span class="tocnumber">1</span> <span class="toctext"> REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)</span></a>
| + | === Dependent variables === |
| − | <ul>
| + | |
| − | <li class='toclevel-2'><a href="#Logistical_Information"><span class="tocnumber">1.1</span> <span class="toctext">Logistical Information</span></a></li>
| + | Percentage of Shallow Comprehension Questions Answered Correctly |
| − | <li class='toclevel-2'><a href="#Abstract"><span class="tocnumber">1.2</span> <span class="toctext">Abstract</span></a></li>
| + | |
| − | <li class='toclevel-2'><a href="#Glossary"><span class="tocnumber">1.3</span> <span class="toctext">Glossary</span></a></li>
| + | Percentage of Deep Comprehension Questions Answered Correctly |
| − | <li class='toclevel-2'><a href="#Research_question"><span class="tocnumber">1.4</span> <span class="toctext">Research question</span></a></li>
| + | |
| − | <li class='toclevel-2'><a href="#Dependent_variables"><span class="tocnumber">1.5</span> <span class="toctext">Dependent variables</span></a></li>
| + | === Independent variables === |
| − | <li class='toclevel-2'><a href="#Independent_variables"><span class="tocnumber">1.6</span> <span class="toctext">Independent variables</span></a></li>
| + | |
| − | <li class='toclevel-2'><a href="#Hypotheses"><span class="tocnumber">1.7</span> <span class="toctext">Hypotheses</span></a></li>
| + | Five reading passages, each of which was followed by four shallow reading comprehension questions and three to five deep reading comprehension questions. |
| − | <li class='toclevel-2'><a href="#Explanation"><span class="tocnumber">1.8</span> <span class="toctext">Explanation</span></a></li>
| + | |
| − | <li class='toclevel-2'><a href="#Notes"><span class="tocnumber">1.9</span> <span class="toctext">Notes</span></a></li>
| + | === Hypothesis === |
| − | <li class='toclevel-2'><a href="#Descendents"><span class="tocnumber">1.10</span> <span class="toctext">Descendents</span></a></li>
| + | |
| − | <li class='toclevel-2'><a href="#Annotated_bibliography"><span class="tocnumber">1.11</span> <span class="toctext">Annotated bibliography</span></a></li>
| + | A significant positive correlation between shallow and deep reading comprehension questions will occur in this study. |
| − | <p>
| + | |
| | + | === Explanation === |
| | + | |
| | + | There was a relationship between performance on the two question types (automatically-generated shallow reading comprehension questions and manually-generated deep comprehension questions), which could imply that using the automatically-generated questions as a check for reading comprehension for the REAP program is a sufficient measure (r = .366, p < 0.0005, one-tailed test). Further studies could examine different types of deep reading comprehension questions, removing the “giveaway words” from the automatically-generated distracters, and examining whether ESL students approach these questions in a different manner than native English speakers. |
| | + | |
| | + | === Descendents === |
| | + | |
| | + | === Notes === |
| | + | |
| | + | The researchers chose five passages of between five hundred to one thousand words because this length is comparable to reading comprehension passages that appear on standardized tests, such as the GRE, and to REAP texts. The researchers used a variety of sources in order to have passages of varying difficulty. |
| | + | |
| | + | === Annotated bibliography === |
| | + | |
| | Curran, James R. Curran & Moens, Marc. (2002a). Improvements in automatic thesaurus extraction. Proceedings of the Workshop of the ACL Special Interest Group on the Lexicon (SIGLEX), 59-66. | | Curran, James R. Curran & Moens, Marc. (2002a). Improvements in automatic thesaurus extraction. Proceedings of the Workshop of the ACL Special Interest Group on the Lexicon (SIGLEX), 59-66. |
| | | | |
| | Davoudi, Mohammad. (2005). Inference Generation Skill and Text Comprehension. The Reading Matrix, 5(1), 106-123. | | Davoudi, Mohammad. (2005). Inference Generation Skill and Text Comprehension. The Reading Matrix, 5(1), 106-123. |
| | | | |
| − | Heilman, M., Collins-Thompson, K., Callan, J. & Eskenazi, M. (2006). Classroom success of an Intelligent Tutoring System for lexical practice and reading comprehension. Proceedingsof the Ninth International Conference on Spoken Language Processing. | + | Heilman, M., Collins-Thompson, K., Callan, J. & Eskenazi, M. (2006). Classroom success of an Intelligent Tutoring System for lexical practice and reading comprehension. Proceedings of the Ninth International Conference on Spoken Language Processing. |
| | | | |
| | Heilman, M. & Eskenazi, M. In Press. Application of Automatic Thesaurus Extraction for Computer Generation of Vocabulary Questions. Proceedings of the SLaTE Workshop on Speech and Language Technology in Education. | | Heilman, M. & Eskenazi, M. In Press. Application of Automatic Thesaurus Extraction for Computer Generation of Vocabulary Questions. Proceedings of the SLaTE Workshop on Speech and Language Technology in Education. |
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| − | </td></tr></table><script type="text/javascript"> if (window.showTocToggle) { var tocShowText = "show"; var tocHideText = "hide"; showTocToggle(); } </script>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=1" title="Edit section: REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)">edit</a>]</div><a name=" REAP_Study_on_the_Correlation_Between_Automatically_and_Manually_Generated_Reading_Comprehension_Questions_"></a><h2> REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions"></</h2>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title= REAP_Study_on_the_Correlation_Between_Automatically_and_Manually_Generated_Reading_Comprehension_Questions_"></%28Summer_2007%29&action=edit&section=2" title="Edit section: Logistical Information">edit</a>]</div><a name="Logistical_Information"></a><h3> Logistical Information </h3>
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| − | <td> <b>Contributors</b> </td><td> Christine Feeney, Michael Heilman, </td></tr>
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| − | <td> <b>Study Start Date</b> </td><td> June, 2007
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| − | </td></tr>
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| − | <td> <b>Study End Date</b> </td><td> July, 2007
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| − | </td></tr>
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| − | <td> <b>Learnlab Courses</b> </td><td> none </td></tr>
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| − | <td> <b>Number of Students</b> </td><td> 30
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| − | </td></tr>
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| − | <td> <b>Total Participant Hours (est.)</b> </td><td> 30
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| − | </td></tr>
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| − | <td> <b>Data in Datashop</b> </td><td> no
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| − | </td></tr></table>
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| − | <p><br />
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=3" title="Edit section: Abstract">edit</a>]</div><a name="Abstract"></a><h3> Abstract </h3>
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| − | <p> Previous psychological research has identified two types of comprehension: shallow, in which people can reproduce information, and deep, in which people can comprehend the meaning of information. In addition, researchers have found that these two types of comprehension are separate and disparate. The PSLC currently employs an English as a Second Language vocabulary tutor, REAP, that uses shallow, automatically generated, comprehension questions to check that students are actively reading, rather than just skimming, practice reading passages. The purpose of the current study was to examine performance on the REAP-type questions with manually authored, deeper, reading comprehension questions. Participants were thirty undergraduate students (male = 11) participating in summer research programs at Carnegie Mellon University. The researcher predicted a positive correlation between the two testing types, which was supported by the data (r = .366, p < 0.0005, one-tailed t-test).
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=4" title="Edit section: Glossary">edit</a>]</div><a name="Glossary"></a><h3> Glossary </h3>
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| − | <p><i>Shallow Comprehension:</i> Processing directed towards reproducing the learning material without necessarily understanding it.
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| − | </p><p><i>Deep Comprehension:</i> Processing directed towards comprehending the intended meaning of the learning material. </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=5" title="Edit section: Research question">edit</a>]</div><a name="Research_question"></a><h3> Research question </h3>
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| − | <p>Does a relationship exist between the two question types?
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title= REAP_Study_on_the_Correlation_Between_Automatically_and_Manually_Generated_Reading_Comprehension_Questions_%28Summer_2007%29&action=edit&section=6" title="Edit section: Dependent variables">edit</a>]</div><a name="Dependent_variables"></a><h3> Dependent variables </h3>
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| − | <p>Percent correct on each question type.
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title= REAP_Study_on_the_Correlation_Between_Automatically_and_Manually_Generated_Reading_Comprehension_Questions_%28Summer_2007%29&action=edit&section=7" title="Edit section: Independent variables">edit</a>]</div><a name="Independent_variables"></a><h3> Independent variables </h3>
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| − | <p>Chosen Reading Passages and question types.
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title= REAP_Study_on_the_Correlation_Between_Automatically_and_Manually_Generated_Reading_Comprehension_Questions_%28Summer_2007%29&action=edit&section=8" title="Edit section: Hypotheses">edit</a>]</div><a name="Hypotheses"></a><h3> Hypotheses </h3>
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| − | <p> The researchers hypothesized that there would be a significant positive correlation between performance on the two question types.
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| − | <p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title= REAP_Study_on_the_Correlation_Between_Automatically_and_Manually_Generated_Reading_Comprehension_Questions_%28Summer_2007%29&action=edit&section=9" title="Edit section: Explanation">edit</a>]</div><a name="Explanation"></a><h3> Explanation </h3>
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| − | <p> The researcher hypothesized a strong positive correlation between performance on shallow and deep reading comprehension questions, which was partially supported by the data analysis. When looking at the participants’ percentages of correct responses for all the questions, these two variables had a medium positive correlation of .366 (p < 0.0005, one-tailed test). This indicates that there is a relationship between the two variables, which can be expressed by the regression equation: Percent Correct of Deep Questions = 0.09 + 0.51 * Percent Correct of Shallow Questions (Figure 2). The R2 value of 0.13 means that overall, thirteen percent of the variance of performance in answering deep comprehension questions can be explained by some mechanism that is at work in answering shallow comprehension questions.
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=10" title="Edit section: Notes">edit</a>]</div><a name="Notes"></a><h3> Notes </h3>
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| − | <p>
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| − | </p>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=11" title="Edit section: Descendents">edit</a>]</div><a name="Descendents"></a><h3> Descendents </h3>
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| − | <div class="editsection" style="float:right;margin-left:5px;">[<a href="/research/wiki/index.php?title=REAP_Study_on_Question_Types_%28Summer_2006%29&action=edit&section=12" title="Edit section: Annotated bibliography">edit</a>]</div><a name="Annotated_bibliography"></a><h3> Annotated bibliography </h3>
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REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)
Logistical Information
| Contributors |
Christine M. Feeney and Michael Heilman
|
| Study Start Date |
June, 2007
|
| Study End Date |
July, 2007
|
| Learnlab Courses |
no
|
| Number of Students |
30
|
| Total Participant Hours (est.) |
300
|
| Data in Datashop |
no
|
Abstract
Previous psychological research has identified two types of comprehension: shallow, in which people can reproduce information, and deep, in which people can comprehend the meaning of information. In addition, researchers have found that these two types of comprehension are separate and disparate. The PSLC currently employs an English as a Second Language vocabulary tutor, REAP, that uses shallow, automatically generated, comprehension questions to check that students are actively reading, rather than just skimming, practice reading passages. The purpose of the current study was to examine performance on the REAP-type questions with manually authored, deeper, reading comprehension questions. Participants were thirty undergraduate students (male = 11) participating in summer research programs at Carnegie Mellon University. The researcher predicted a positive correlation between the two testing types, which was supported by the data (r = .366, p < 0.0005, one-tailed t-test).
Glossary
Shallow Comprehension: Processing directed towards reproducing the learning material without necessarily understanding it.
Deep Comprehension: Processing directed towards comprehending the intended meaning of the learning material.
Research question
Does performance on the shallow, automatically-generated questions correlate with performance on more sophisticated, deep reading comprehension questions?
Dependent variables
Percentage of Shallow Comprehension Questions Answered Correctly
Percentage of Deep Comprehension Questions Answered Correctly
Independent variables
Five reading passages, each of which was followed by four shallow reading comprehension questions and three to five deep reading comprehension questions.
Hypothesis
A significant positive correlation between shallow and deep reading comprehension questions will occur in this study.
Explanation
There was a relationship between performance on the two question types (automatically-generated shallow reading comprehension questions and manually-generated deep comprehension questions), which could imply that using the automatically-generated questions as a check for reading comprehension for the REAP program is a sufficient measure (r = .366, p < 0.0005, one-tailed test). Further studies could examine different types of deep reading comprehension questions, removing the “giveaway words” from the automatically-generated distracters, and examining whether ESL students approach these questions in a different manner than native English speakers.
Descendents
Notes
The researchers chose five passages of between five hundred to one thousand words because this length is comparable to reading comprehension passages that appear on standardized tests, such as the GRE, and to REAP texts. The researchers used a variety of sources in order to have passages of varying difficulty.
Annotated bibliography
Curran, James R. Curran & Moens, Marc. (2002a). Improvements in automatic thesaurus extraction. Proceedings of the Workshop of the ACL Special Interest Group on the Lexicon (SIGLEX), 59-66.
Davoudi, Mohammad. (2005). Inference Generation Skill and Text Comprehension. The Reading Matrix, 5(1), 106-123.
Heilman, M., Collins-Thompson, K., Callan, J. & Eskenazi, M. (2006). Classroom success of an Intelligent Tutoring System for lexical practice and reading comprehension. Proceedings of the Ninth International Conference on Spoken Language Processing.
Heilman, M. & Eskenazi, M. In Press. Application of Automatic Thesaurus Extraction for Computer Generation of Vocabulary Questions. Proceedings of the SLaTE Workshop on Speech and Language Technology in Education.
