  Knowledge tracing allows Intelligent Tutoring Systems to infer which topics
or skills a student has mastered, thus adjusting curriculum accordingly. Deep
Learning based models like Deep Knowledge Tracing (DKT) and Dynamic Key-Value
Memory Network (DKVMN) have achieved significant improvements compared with
models like Bayesian Knowledge Tracing (BKT) and Performance Factors Analysis
(PFA). However, these deep learning based models are not as interpretable as
other models because the decision-making process learned by deep neural
networks is not wholly understood by the research community. In previous work,
we critically examined the DKT model, visualizing and analyzing the behaviors
of DKT in high dimensional space. In this work, we extend our original analyses
with a much larger dataset and add discussions about the memory states of the
DKVMN model. We discover that Deep Knowledge Tracing has some critical
pitfalls: 1) instead of tracking each skill through time, DKT is more likely to
learn an `ability' model; 2) the recurrent nature of DKT reinforces irrelevant
information that it uses during the tracking task; 3) an untrained recurrent
network can achieve similar results to a trained DKT model, supporting a
conclusion that recurrence relations are not properly learned and, instead,
improvements are simply a benefit of projection into a high dimensional, sparse
vector space. Based on these observations, we propose improvements and future
directions for conducting knowledge tracing research using deep neural network
models.
