  Knowledge tracing (KT) has recently been an active research area of
computational pedagogy. The task is to model students' mastery level of
knowledge concepts 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. 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 the explicit modeling of the learning curve theory, which generally
says that more practice on the same knowledge concept enhances one's mastery
level of the concept. Based on this theory, we propose a Convolution-Augmented
Knowledge Tracing (CAKT) model in this paper. The model employs
three-dimensional convolutional neural networks to explicitly learn a student's
recent experience on applying the same knowledge concept with that in the next
question, and fuses the learnt feature with the feature representing her
overall latent knowledge state obtained using a classic LSTM network. The fused
feature is then fed into a second LSTM network to predict the student's
response to the next question. Experimental results show that CAKT achieves the
new state-of-the-art performance in predicting students' responses compared
with existing models. We also conduct extensive sensitivity analysis and
ablation study to show the stability of the results and justify the particular
architecture of CAKT, respectively.
