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- W1569598217 abstract "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 dissertation 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. I report classification accuracies between 23% and 56%, obtaining substantial improvements by exploiting domain knowledge (compiler error messages) and educational context (assignment name). My 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." @default.
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- W1569598217 date "2007-06-08" @default.
- W1569598217 modified "2023-09-24" @default.
- W1569598217 title "Towards a virtual teaching assistant to answer questions asked by students in introductory computer science" @default.
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