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ERIC Number: ED560760
Record Type: Non-Journal
Publication Date: 2015-Jun
Pages: 4
Abstractor: As Provided
Reference Count: 13
Spectral Bayesian Knowledge Tracing
Falakmasir, Mohammad; Yudelson, Michael; Ritter, Steve; Koedinger, Ken
International Educational Data Mining Society, Paper presented at the International Conference on Educational Data Mining (EDM) (8th, Madrid, Spain, Jun 26-29, 2015)
Bayesian Knowledge Tracing (BKT) has been in wide use for modeling student skill acquisition in Intelligent Tutoring Systems (ITS). BKT tracks and updates student's latent mastery of a skill as a probability distribution of a binary variable. BKT does so by accounting for observed student successes in applying the skill correctly, where success is also treated as a binary variable. While the BKT served the ITS community well, representing both the latent state and the observed performance as binary variables is, nevertheless, a simplification. In addition, BKT as a two-state and two-observation first-order HMM is prone to noise in the data. In this paper, we present work that uses feature compensation and model compensation paradigms in an attempt to conceptualize a more flexible and robust BKT model. Validation of this approach on the KDD Cup 2010 data shows a tangible boost in model accuracy well over the improvements reported in the literature. [This research paper was partially supported by the Andrew Mellon Pre-doctoral Fellowship and extends a project initiated during the Pittsburgh Science of Learning Center's Summer School at Carnegie Mellon University. For complete proceedings, see ED560503.]
International Educational Data Mining Society. e-mail:; Web site:
Publication Type: Speeches/Meeting Papers; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: International Educational Data Mining Society