ABSTRACT
An ongoing study is being run to ensure that the McGraw-Hill Education LearnSmart platform teaches students as efficiently as possible. The first step in doing so is to identify what Knowledge Components (KCs) exist in the content; while the content is tagged by experts, these tags need to be re-calibrated periodically.
LearnSmart courses are organized into chapters corresponding to those found in a textbook; each chapter can have anywhere from about a hundred to a few thousand questions. The KC extraction algorithms proposed by Barnes [1] and Desmarais et al [3] are applied on a chapter-by-chapter basis. To assess the ability of each mined q matrix to describe the observed learning, the PFA model of Pavlik et al [4] is fitted to it and a cross-validated AUC is calculated. The models are assessed based on whether PFA's predictions of student correctness are accurate.
Early results show that both algorithms do a reasonable job of describing student progress, but q matrices with very different numbers of KCs fit observed data similarly well. Consequently, further consideration is required before automated extraction is practical in this context.
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- M. C. Desmarais, B. Beheshti, and R. Naceur. Item to skills mapping: Deriving a conjunctive q-matrix from data. In International Conference on Intelligent Tutoring Systems, pages 454--463. Springer Science & Business Media, 2012. Google ScholarDigital Library
- P. I. Pavlik, H. Cen, and K. R. Koedinger. Performance factors analysis-a new alternative to knowledge tracing. In Proceedings of the 2009 Conference on Artificial Intelligence in Education: Building Learning Systems That Care: From Knowledge Representation to Affective Modelling, pages 531--538, Amsterdam, The Netherlands, The Netherlands, 2009. IOS Press. Google ScholarDigital Library
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Index Terms
- Mining knowledge components from many untagged questions
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