Deep Knowledge Tracing Model with the Integration of Skills Relation and Forgetting Degree

Knowledge tracing is a pivotal technique for modeling students' knowledge level, typically relies on their past learning interactions to predict their future performance on exercises. These interactions represent a student's process of answering a sequence of questions. Current knowledge tracing methods ignore the number of times a skill has been practiced when modeling student’s forgetting behaviors. Also, few models consider the relation between skills and its influence on performance prediction. To address these questions, we propose a deep knowledge tracing model with the integration of skills relation and forgetting degree. Firstly, a relation matrix is constructed using statistical methods to capture the relation between skills. Secondly, the time intervals between interactions and the number of times a student practices the same skill are used to compute the forgetting degree of each skill for better modeling of students' forgetting behaviors. Finally, skills relation and forgetting degrees are integrated into an attention module to obtain the influence of each past interaction on future performance prediction. Based on new attention weights, students' performance on future exercises and kon can be predicted. Experiments on two real-world online education datasets, algebra2005-2006 and ASSISTment 2012, demonstrate that the proposed model achieves better prediction results compared to existing mainstream methods.