A Novel Knowledge Tracing Model Based on Collaborative Multi-Head Attention

Abstract

Online education is playing a more and more important role in today's education. The key link of online education is to model students' knowledge mastery according to their historical behaviors, so as to obtain the knowledge tracing represented by students' current knowledge state. Previous Transformer-based knowledge tracing models have disadvantages such as inefficient model computation and redundant information on the one hand. On the other hand, the traditional knowledge tracing model cannot solve the problem of imbalanced positive and negative samples in the data well. In order to better model the current knowledge state of students, this paper proposes a knowledge tracing model based on the collaborative multi-head attention mechanism. The model uses a collaborative multi-head attention mechanism to solve the information redundancy problem in the previous Transformer-based knowledge tracing model, and improves the computational efficiency and performance of the model. The model also introduces a focal loss function, which not only solves the problem of imbalanced question labeling divisions in knowledge tracing but also improves the differentiation of difficulty level among the questions and enhances the accuracy of model prediction. The experimental results on three public experimental datasets show that the knowledge tracing model based on the collaborative multi-head attention mechanism proposed in this paper outperforms other recent knowledge tracing models in terms of evaluation metric AUC and also has better performance in predicting students' responses.