  For offering proactive services to students in intelligent education, one of
the fundamental tasks is predicting their performance (e.g., scores) on future
exercises, where it is necessary to track each student's knowledge acquisition
during her exercising activities. However, existing approaches can only exploit
the exercising records of students, and the problem of extracting rich
information existed in the exercise's materials (e.g., knowledge concepts,
exercise content) to achieve both precise predictions of student performance
and interpretable analysis of knowledge acquisition remains underexplored. In
this paper, we present a holistic study of student performance prediction. To
directly achieve the primary goal of prediction, we first propose a general
Exercise-Enhanced Recurrent Neural Network (EERNN) framework by exploring both
student's records and the exercise contents. In EERNN, we simply summarize each
student's state into an integrated vector and trace it with a recurrent neural
network, where we design a bidirectional LSTM to learn the encoding of each
exercise's content. For making predictions, we propose two implementations
under EERNN with different strategies, i.e., EERNNM with Markov property and
EERNNA with Attention mechanism. Then, to explicitly track student's knowledge
acquisition on multiple knowledge concepts, we extend EERNN to an explainable
Exercise-aware Knowledge Tracing (EKT) by incorporating the knowledge concept
effects, where the student's integrated state vector is extended to a knowledge
state matrix. In EKT, we further develop a memory network for quantifying how
much each exercise can affect the mastery of students on concepts during the
exercising process. Finally, we conduct extensive experiments on large-scale
real-world data. The results demonstrate the prediction effectiveness of two
frameworks as well as the superior interpretability of EKT.
