Enhanced Dynamic Key-Value Memory Networks for Personalized Student Modeling and Learning Ability Classification

Knowledge tracing (KT) is a technique that can be applied to predict students’ current skill mastery levels and future academic performance based on previous question-answering data. A good KT model can more accurately reflect a student’s cognitive processes and provide a more realistic assessment of skill mastery level. Currently, most KT models regard all students as a whole, while ignoring their personal differences; a few KT models attempt to personalize the modeling of students from the perspective of their learning abilities, among which a typical example is Deep Knowledge Tracing with Dynamic Student Classification (DKT-DSC). However, these models have a relatively coarse-grained approach to modeling students’ learning abilities and cannot accurately capture the nonlinear relationship between students’ learning abilities and the questions they answer. To solve these problems, we propose a novel KT model named the Enhanced Dynamic Key-Value Memory Networks for Dynamic Student Classification (EnDKVMN-DSC). This model is specifically designed for personalized student modeling and learning ability classification. Specifically, first, we propose a novel Enhanced Dynamic Key-Value Memory Network (EnDKVMN) and use it to model each student’s learning ability. Second, students are classified according to their learning abilities based on the K-means algorithm. Finally, the enriched input features are constructed and passed through Gated Recurrent Unit (GRU) networks to obtain prediction results. All experiments are conducted on four real-world datasets to evaluate our proposed model, and the results show that EnDKVMN-DSC outperforms the other four state-of-the-art KT models based on DKT or DKVMN in predicting student performance.