Variational Deep Knowledge Tracing for Language Learning

Deep Knowledge Tracing (DKT), which traces a student’s knowledge change using deep recurrent neural networks, is widely adopted in student cognitive modeling. Current DKT models only predict a student’s performance based on the observed learning history. However, a student’s learning processes often contain latent events not directly observable in the learning history, such as partial understanding, making slips, and guessing answers. Current DKT models fail to model this kind of stochasticity in the learning process. To address this issue, we propose Variational Deep Knowledge Tracing (VDKT), a latent variable DKT model that incorporates stochasticity into DKT through latent variables. We show that VDKT outperforms both a sequence-to-sequence DKT baseline and previous SoTA methods on MAE, F1, and AUC by evaluating our approach on two Duolingo language learning datasets. We also draw various interpretable analyses from VDKT and offer insights into students’ stochastic behaviors in language learning.