Learner Profile based Knowledge Tracing

In recent years, knowledge tracing has gradually become a core technology for online education, which can evaluate learners' knowledge states and provide a personalized learning path. Most of the existing knowledge tracing methods mainly considered the interaction sequence of learners, but they normally ignored the individual differences among learners. For example, learners with various levels of comprehension will behave differently when faced with new questions, which indicates that individual differences affect prediction accuracy. In addition, most learners learn only part of the concept, which leads to data sparsity. However, the existing methods do not solve the data sparsity well. In this paper, we are motivated to propose a Learner Profile-based Knowledge Tracing (LPKT) model, which uses learners' unique id and the features extracted from historical interaction sequences as learners' representation to model individual differences among learners. In addition, we establish relationships between concepts and utilize related concepts to augment the concept's representation to address the data sparsity. We conducted experiments on several benchmark datasets, and the results show that our proposed LPKT model outperforms existing KT methods (with the highest AUC improvement of up to 8%).