REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)

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REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)

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Contents

  • <a href="#REAP_Study_on_Question_Types">1 REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions (Summer 2007)</a>
    • <a href="#Logistical_Information">1.1 Logistical Information</a>
    • <a href="#Abstract">1.2 Abstract</a>
    • <a href="#Glossary">1.3 Glossary</a>
    • <a href="#Research_question">1.4 Research question</a>
    • <a href="#Dependent_variables">1.5 Dependent variables</a>
    • <a href="#Independent_variables">1.6 Independent variables</a>
    • <a href="#Hypotheses">1.7 Hypotheses</a>
    • <a href="#Explanation">1.8 Explanation</a>
    • <a href="#Notes">1.9 Notes</a>
    • <a href="#Descendents">1.10 Descendents</a>
    • <a href="#Annotated_bibliography">1.11 Annotated bibliography</a>

      • 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. Proceedingsof 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.

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REAP Study on the Correlation Between Automatically and Manually Generated Reading Comprehension Questions"></

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Logistical Information

Contributors Christine Feeney, Michael Heilman,
Study Start Date June, 2007
Study End Date July, 2007
Learnlab Courses none
Number of Students 30
Total Participant Hours (est.) 30
Data in Datashop no


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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).

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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.

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Research question

Does a relationship exist between the two question types?

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Dependent variables

Percent correct on each question type.

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Independent variables

Chosen Reading Passages and question types.

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Hypotheses

The researchers hypothesized that there would be a significant positive correlation between performance on the two question types.

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Explanation

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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Notes

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Descendents

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Annotated bibliography

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