Deep Knowledge Tracing with Convolutions

Knowledge tracing (KT) has recently been an active research area of computational pedagogy. The task is to model students mastery level of knowledge based on their responses to the questions in the past, as well as predict the probabilities that they correctly answer subsequent questions in the future. A good KT model can not only make students timely aware of their knowledge states, but also help teachers develop better personalized teaching plans for students. KT tasks were historically solved using statistical modeling methods such as Bayesian inference and factor analysis, but recent advances in deep learning have led to the successive proposals that leverage deep neural networks, including long short-term memory networks, memory-augmented networks and self-attention networks. While those deep models demonstrate superior performance over the traditional approaches, they all neglect more or less the impact on knowledge states of the most recent questions answered by students. The forgetting curve theory states that human memory retention declines over time, therefore knowledge states should be mostly affected by the recent questions. Based on this observation, we propose a Convolutional Knowledge Tracing (CKT) model in this paper. In addition to modeling the long-term effect of the entire question-answer sequence, CKT also strengthens the short-term effect of recent questions using 3D convolutions, thereby effectively modeling the forgetting curve in the learning process. Extensive experiments show that CKT achieves the new state-of-the-art in predicting students performance compared with existing models. Using CKT, we gain 1.55 and 2.03 improvements in terms of AUC over DKT and DKVMN respectively, on the ASSISTments2009 dataset. And on the ASSISTments2015 dataset, the corresponding improvements are 1.01 and 1.96 respectively.