Profiling Student Interactions in Threaded Discussions with Speech Act Classifiers

  • Authors:
  • Sujith Ravi;Jihie Kim

  • Affiliations:
  • University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA 90292 USA;University of Southern California/Information Sciences Institute, 4676 Admiralty Way, Marina del Rey, CA 90292 USA

  • Venue:
  • Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
  • Year:
  • 2007

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Abstract

On-line discussion is a popular form of web-based computer-mediated communication and is an important medium for distance education. Automatic tools for analyzing online discussions are highly desirable for better information management and assistance. This paper presents an approach for automatically profiling student interactions in on-line discussions. Using N-gram features and linear SVM, we developed “speech act” classifiers that identify the roles that individual messages play. The classifiers were used in finding messages that contain questions or answers. We then applied a set of thread analysis rules for identifying threads that may have unanswered questions and need instructor attention. We evaluated the results with three human annotators, and 70-75% of the predictions from the system were consistent with human answers.