Research on Deep Knowledge Tracing Model Integrating Graph Attention Network

Abstract

The current mainstream knowledge tracking model is based on the neural network of deep learning, which has a certain improvement in performance. However, due to the difficulty of interpretability of the deep learning methods, and the previous literature did not involve the high-dimensional information between problems and knowledge points when their model used the answer record, there is a situation that the relevant information is not sufficiently extracted. In order to solve the above problems, a knowledge tracing model based on the graph attention network mechanism is proposed, which uses the graph attention network to reveal the potential graph structure between knowledge points in answer records, and aggregates the correlation degree through the attention mechanism, so that the input information of the model includes the relationship information between problems and knowledge points, which enhances the interpretability of the model and improves the prediction accuracy of the model. On the three commonly used public datasets, the proposed model can better reflect learners’ mastery of knowledge points.