An Empirical Comparison of Deep Learning Models for Knowledge Tracing on
  Large-Scale Dataset
  Knowledge tracing (KT) is the problem of modeling each student's mastery of
knowledge concepts (KCs) as (s)he engages with a sequence of learning
activities. It is an active research area to help provide learners with
personalized feedback and materials. Various deep learning techniques have been
proposed for solving KT. Recent release of large-scale student performance
dataset \cite{choi2019ednet} motivates the analysis of performance of deep
learning approaches that have been proposed to solve KT. Our analysis can help
understand which method to adopt when large dataset related to student
performance is available. We also show that incorporating contextual
information such as relation between exercises and student forget behavior
further improves the performance of deep learning models.
