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ERIC Number: ED539086
Record Type: Non-Journal
Publication Date: 2009-Jul
Pages: 10
Abstractor: As Provided
Reference Count: 14
ISBN: N/A
ISSN: N/A
Improving Student Question Classification
Heiner, Cecily; Zachary, Joseph L.
International Working Group on Educational Data Mining, Paper presented at the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, Jul 1-3, 2009)
Students in introductory programming classes often articulate their questions and information needs incompletely. Consequently, the automatic classification of student questions to provide automated tutorial responses is a challenging problem. This paper analyzes 411 questions from an introductory Java programming course by reducing the natural language of the questions to a vector space, and then utilizing cosine similarity to identify similar previous questions. We report classification accuracies between 23% and 55%, obtaining substantial improvements by exploiting domain knowledge (compiler error messages) and educational context (assignment name). Our mean reciprocal rank scores are comparable to and arguably better than most scores reported in a major information retrieval competition, even though our dataset consists of questions asked by students that are difficult to classify. Our results are especially timely and relevant for online courses where students are completing the same set of assignments asynchronously and access to staff is limited. (Contains 1 figure and 2 tables.) [For the complete proceedings, "Proceedings of the International Conference on Educational Data Mining (EDM) (2nd, Cordoba, Spain, July 1-3, 2009)," see ED539041.]
International Working Group on Educational Data Mining. Available from: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: http://www.educationaldatamining.org
Publication Type: Reports - Evaluative; Speeches/Meeting Papers
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: National Science Foundation
Authoring Institution: International Working Group on Educational Data Mining
Identifiers - Location: Utah