Group Discovery with Multiple-Choice Exams and Consumer Surveys: The Group-Question-Answer Model

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Ian D. Beatty, Assistant Professor (Creator)
William Gerace, Helena Gabriel Houston Distinguished Professor (Creator)
The University of North Carolina at Greensboro (UNCG )
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Abstract: Multiple choice questions (MCQs) are a common data gathering tool. We extend the Latent Dirichlet Allocation (LDA) framework to a collection of MCQ surveys. Topic discovery is turned into group discovery based on survey response patterns. Question choices are equivalent to vocabulary words and are conditioned on the question and the latent group that is used to cluster the survey responders. The structured format of MCQ surveys creates correlations between document ‘authors’ not found in unstructured natural language documents. We demonstrate the utility of the model by considering two performance measures: How well can we predict held-out question answers? What is the discriminatory power of the survey questions? The model should be of interest to anybody that uses MCQ surveys or exams to identify social groups.

Additional Information

University of Massachusetts Amherst
Language: English
Date: 2007
Multiple choice questions (MCQs), data gathering tools, Latent Dirichlet Allocation (LDA), surveys, social groups

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