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Group Discovery with Multiple-Choice Exams and Consumer Surveys: The Group-Question-Answer Model
Computer Science Department Technical Report (2007)
  • Andres Corrada-Emmanuel, University of Massachusetts - Amherst
  • Ian Beatty, University of Massachusetts - Amherst
  • William Gerace, University of Massachusetts - Amherst
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.
Keywords
  • multiple choice exams,
  • consumer surveys,
  • latent dirichlet allocation,
  • exam cheating
Disciplines
Publication Date
September 18, 2007
Citation Information
Andres Corrada-Emmanuel, Ian Beatty and William Gerace. "Group Discovery with Multiple-Choice Exams and Consumer Surveys: The Group-Question-Answer Model" Computer Science Department Technical Report (2007)
Available at: http://works.bepress.com/corrada_andres/1/