  Accurate modeling of student knowledge is essential for large-scale online
learning systems that are increasingly used for student training. Knowledge
tracing aims to model student knowledge state given the student's sequence of
learning activities. Modern Knowledge tracing (KT) is usually formulated as a
supervised sequence learning problem to predict students' future practice
performance according to their past observed practice scores by summarizing
student knowledge state as a set of evolving hidden variables. Because of this
formulation, many current KT solutions are not fit for modeling student
learning from non-assessed learning activities with no explicit feedback or
score observation (e.g., watching video lectures that are not graded).
Additionally, these models cannot explicitly represent the dynamics of
knowledge transfer among different learning activities, particularly between
the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning
activities. In this paper, we propose Transition-Aware Multi-activity Knowledge
Tracing (TAMKOT), which models knowledge transfer between learning materials,
in addition to student knowledge, when students transition between and within
assessed and non-assessed learning materials. TAMKOT is formulated as a deep
recurrent multi-activity learning model that explicitly learns knowledge
transfer by activating and learning a set of knowledge transfer matrices, one
for each transition type between student activities. Accordingly, our model
allows for representing each material type in a different yet transferrable
latent space while maintaining student knowledge in a shared space. We evaluate
our model on three real-world publicly available datasets and demonstrate
TAMKOT's capability in predicting student performance and modeling knowledge
transfer.
