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Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning
Educational Data Mining
Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions, they differ in their parameterization of student learning. One key difference is that BKT has parameters for the slipping rates of learned skills,dblp:conf/edm/MacLellanLK15 fatcat:4zgbr2wbcveqbpsqbmbqfyibom