Variational Deep Knowledge Tracing for Language Learning Sherry Ruan Wei Wei James A. Landay
[email protected] [email protected] [email protected] Stanford University Google Stanford University Stanford, California, United States Sunnyvale, California, United States Stanford, California, United States ABSTRACT You are✔ buying shoes Deep Knowledge Tracing (DKT), which traces a student’s knowl- edge change using deep recurrent neural networks, is widely adopted in student cognitive modeling. Current DKT models only predict You are✘ young Yes you are✘ going 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 under- You are✔ You are✔ You are✔ You are✔ standing, making slips, and guessing answers. Current DKT models not going not real eating cheese welcome 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 Linking verb Tense ✔/✘ Right/Wrong Attempt into DKT through latent variables. We show that VDKT outper- forms both a sequence-to-sequence DKT baseline and previous Figure 1: A probabilistic knowledge tracing system tracks SoTA methods on MAE, F1, and AUC by evaluating our approach the distribution of two concepts concerning the word are: on two Duolingo language learning datasets. We also draw various understanding it as a linking verb and understanding it in interpretable analyses from VDKT and offer insights into students’ the present progressive tense. 3 and 7 represent whether stochastic behaviors in language learning.