  With the increasing demands of personalized learning, knowledge tracing has
become important which traces students' knowledge states based on their
historical practices. Factor analysis methods mainly use two kinds of factors
which are separately related to students and questions to model students'
knowledge states. These methods use the total number of attempts of students to
model students' learning progress and hardly highlight the impact of the most
recent relevant practices. Besides, current factor analysis methods ignore rich
information contained in questions. In this paper, we propose Multi-Factors
Aware Dual-Attentional model (MF-DAKT) which enriches question representations
and utilizes multiple factors to model students' learning progress based on a
dual-attentional mechanism. More specifically, we propose a novel
student-related factor which records the most recent attempts on relevant
concepts of students to highlight the impact of recent exercises. To enrich
questions representations, we use a pre-training method to incorporate two
kinds of question information including questions' relation and difficulty
level. We also add a regularization term about questions' difficulty level to
restrict pre-trained question representations to fine-tuning during the process
of predicting students' performance. Moreover, we apply a dual-attentional
mechanism to differentiate contributions of factors and factor interactions to
final prediction in different practice records. At last, we conduct experiments
on several real-world datasets and results show that MF-DAKT can outperform
existing knowledge tracing methods. We also conduct several studies to validate
the effects of each component of MF-DAKT.
