Incremental Context Aware Attentive Knowledge Tracing

Abstract

Knowledge Tracing is the prediction of the future performance of a learner, given the past performance. The existing knowledge tracing models represent the training data and does not generalize when there is a drift in the data distribution. We first empirically demonstrate an evolving Knowledge Tracing (eKT) scenario with distinct distribution of learner performances and diversity of questions from similar concepts. Next, we empirically characterize drift in the data and propose a task agnostic incremental context aware attentive knowledge tracing (iAKT) approach to learn incrementally from the eKT. The iAKT regularizes representations to learn from diverse learner performance distributions. Finally, we evaluate the ability of the proposed iAKT for knowledge tracing and study the effect of various regularization strategies on ranking difficulty of questions, using the ASSISTments 2017 data set. Performance results show that the iAKT adapts its representations to drift in data characteristics, while iAKT with EWC regularizer is better at ranking the difficulty of questions.