DKVMN-KAPS: Dynamic Key-Value Memory Networks Knowledge Tracing With Students’ Knowledge-Absorption Ability and Problem-Solving Ability

Abstract

Knowledge tracing aims to predict students' future question-answering performance based on their historical question-answering records, but the current mainstream knowledge tracing model ignores the individual differences in different students' knowledge-absorption and problem-solving abilities, which leads to a poor prediction of students' question-answering performance by the model. To solve this, Dynamic Key-Value Memory Networks Knowledge Tracing with Students' Knowledge-Absorption Ability and Problem-Solving Ability (DKVMN-KAPS) is proposed in this paper. Firstly, a hierarchical convolutional neural network is used to consider students' knowledge mastery at multiple time steps, and then quantify students' knowledge-absorption ability, aiming to more accurately portray students' knowledge states; secondly, an autoencoder is used to dynamically update students' problem-solving ability at each time step; and finally, students' question answering performance is predicted by considering the students' knowledge state, problemsolving ability, and question features. Extensive experiments on three datasets show that the prediction performance of DKVMN-KAPS outperforms existing models and improves the prediction accuracy of deep knowledge tracing models.INDEX TERMS knowledge tracing, knowledge-absorption ability, problem-solving ability, individual differences.