Question Feature Enhanced Knowledge Tracing Model

Abstract

Knowledge tracing involves tracking students’ knowledge state in real-time based on their past answering performance and predicting their future performance, which is crucial for personalized education. In recent years, RNN-based deep knowledge tracing models have gradually become the mainstream research approach in the field of knowledge tracing. However, existing knowledge tracing models suffer from the inability to capture long-term dependencies between sequences and the neglect of the relationship between questions and knowledge points, resulting in insufficient extraction of question features. To address these issues, a knowledge tracing model based on question feature enhancement (QFEKT) is proposed. Firstly, graph convolutional neural network is used to model the relevant features of questions and knowledge points, and contrastive learning is introduced to enhance feature representation. Then, the question features are further enhanced through question matching module and student knowledge state representation module. The question matching module extracts similar questions as a complement to the question features. The student knowledge state representation module combines bidirectional long short-term memory networks with attention mechanism to enhance question features and model students’ knowledge state. Finally, the prediction module integrates similar question features and student knowledge state to predict students’ future performance. Comparative experiments on three publicly available real-world datasets demonstrate that the QFEKT model outperforms other baseline models in knowledge tracing tasks and exhibits significant advantages in predicting students’ future performance.

Keywords

• knowledge tracing; feature enhancement; graph convolutional neural network; contrastive learning; attention mechanism