  Knowledge tracing (KT) refers to the problem of predicting future learner
performance given their past performance in educational applications. Recent
developments in KT using flexible deep neural network-based models excel at
this task. However, these models often offer limited interpretability, thus
making them insufficient for personalized learning, which requires using
interpretable feedback and actionable recommendations to help learners achieve
better learning outcomes. In this paper, we propose attentive knowledge tracing
(AKT), which couples flexible attention-based neural network models with a
series of novel, interpretable model components inspired by cognitive and
psychometric models. AKT uses a novel monotonic attention mechanism that
relates a learner's future responses to assessment questions to their past
responses; attention weights are computed using exponential decay and a
context-aware relative distance measure, in addition to the similarity between
questions. Moreover, we use the Rasch model to regularize the concept and
question embeddings; these embeddings are able to capture individual
differences among questions on the same concept without using an excessive
number of parameters. We conduct experiments on several real-world benchmark
datasets and show that AKT outperforms existing KT methods (by up to $6\%$ in
AUC in some cases) on predicting future learner responses. We also conduct
several case studies and show that AKT exhibits excellent interpretability and
thus has potential for automated feedback and personalization in real-world
educational settings.
