Joint topic modeling and factor analysis of textual information and graded response data

Andrew S. Lan, Christoph Studer, Andrew E. Waters, Richard G. Baraniuk

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Modern machine learning methods are critical to the development of large-scale personalized learning systems that cater directly to the needs of individual learners. The recently developed SPARse Factor Analysis (SPARFA) framework jointly estimates learner’s knowledge of the latent concepts underlying a domain and the relationships among a collection of questions and the latent concepts, solely from the graded responses to a collection of questions. To better interpret the estimated latent concepts, SPARFA relies on a post-processing step that utilizes user-defined tags (e.g., topics or keywords) available for each question. In this paper, we relax the need for user-defined tags by extending SPARFA to jointly process both graded learner responses and the text of each question and its associated answer(s) or other feedback. Our purely data-driven approach (i) enhances the interpretability of the estimated latent concepts without the need of explicitly generating a set of tags or performing a post-processing step, (ii) improves the prediction performance of SPARFA, and (iii) scales to large test/assessments where human annotation would prove burdensome. We demonstrate the efficacy of the proposed approach on two real educational datasets.

Original languageEnglish (US)
Title of host publicationProceedings of the 6th International Conference on Educational Data Mining, EDM 2013
EditorsSidney K. D'Mello, Rafael A. Calvo, Andrew Olney
PublisherInternational Educational Data Mining Society
ISBN (Electronic)9780983952527
StatePublished - Jan 1 2013
Event6th International Conference on Educational Data Mining, EDM 2013 - Memphis, United States
Duration: Jul 6 2013Jul 9 2013

Other

Other6th International Conference on Educational Data Mining, EDM 2013
CountryUnited States
CityMemphis
Period7/6/137/9/13

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems

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