Calibrated Q-Matrix-Enhanced Deep Knowledge Tracing with Relational Attention Mechanism

With the development of online educational platforms, numerous research works have focused on the <i>knowledge tracing</i> task, which relates to the problem of diagnosing the changing knowledge proficiency of learners. Deep-neural-network-based models are used to explore the interaction information between students and their answer logs in the current field of knowledge tracing studies. However, those models ignore the impact of previous interactions, including the exercise relation, forget factor, and student behaviors (the slipping factor and the guessing factor). Those models also do not consider the importance of the Q-matrix, which relates exercises to knowledge points. In this paper, we propose a novel relational attention knowledge tracing (RAKT) to track the students’ knowledge proficiency in exercises. Specifically, the RAKT model incorporates the students’ performance data with corresponding interaction information, such as the context of exercises and the different time intervals between exercises. The RAKT model also takes into account the students’ interaction behaviors, including the slipping factor and the guessing factor. Moreover, consider the relationship between exercise sets and knowledge sets and the relationship between different knowledge points in the same exercise. An extension model of RAKT is called the Calibrated Q-matrix relational attention knowledge tracing model (QRAKT), which was developed using a Q-matrix calibration method based on the hierarchical knowledge levels. Experiments were conducted on two public educational datasets, ASSISTment2012 and Eedi. The results of the experiments indicated that the RAKT model and the QRAKT model outperformed the four baseline models.