DKT-LCIRT: A Deep Knowledge Tracking Model Integrating Learning Capability and Item Response Theory

Abstract

In the realm of intelligent education, knowledge tracking is a critical study topic. Deep learning-based knowledge tracking models have better predictive performance compared to traditional knowledge tracking models, but the models are less interpretable and also often ignore the intrinsic differences among students (e.g., learning capability, guessing capability, etc.), resulting in a lack of personalization of predictive results. To further reflect the personalized differences among students and enhance the interpretability of the model at the same time, a Deep Knowledge Tracking model integrating Learning Capability and Item Response Theory (DKT-LCIRT) is proposed. The model dynamically calculates students’ learning capability by each time interval and allocates each student to groups with similar learning capabilities to increase the predictive performance of the model. Furthermore, the model introduces item response theory to enhance the interpretability of the model. Substantial experiments on four real datasets were carried out, and the experimental results showed that the DKT-LCIRT model improved the AUC by 3% and the ACC by 2% compared to other models. The results confirmed that the DKT-LCIRT model outperformed other classical models in terms of predictive performance, fully reflecting students’ individualization and adding a more meaningful interpretation to the model.