关键词: 智慧教育, 深度知识追踪, 可解释性, 知识追踪模型, 深度学习

Abstract: Knowledge tracing was a cognitive diagnostic method aimed at simulating learner's mastery of learning materials by analyzing their historical response records, ultimately predicting their future performance. With the development of deep learning technology, knowledge tracing techniques based on deep neural network models have become a hot research topic in the field due to their strong feature extraction capabilities and superior predictive performance. However, deep learning-based knowledge tracing models often lack good interpretability. Nevertheless, clear interpretability not only enables learners and teachers to fully understand the reasoning process and prediction results of knowledge tracing models, thus facilitating the formulation of learning plans tailored to the current knowledge state for future learning, but also enhances the trust of learners and teachers in knowledge tracing models. Therefore, first introduces the development of knowledge tracing and the definition and necessity of interpretability. Secondly, it summarizes and organizes improvement methods proposed for the lack of interpretability in deep knowledge tracing models from the perspectives of feature extraction and internal model enhancement. Thirdly, it introduces publicly available datasets for researchers and analyzes the impact of dataset features on interpretability, discussing how to evaluate knowledge tracing models from both performance and interpretability perspectives, and compiling the performance of models on different datasets. Finally, it proposes some possible future research directions to address current issues in the field of deep knowledge tracing models.

Key words: wisdom education, deep knowledge tracing, explainability, knowledge tracing model, deep learning