Knowledge Tracing Model and Student Profile Based on Clustering-Neural-Network

Knowledge tracing models based on deep neural networks are currently widely studied to enhance personalized learning. However, to ensure the practical deployment of DNN-based KT models, prediction accuracy, training efficiency, and interpretability should be greatly improved. In this paper, we observe that the prediction accuracy of KT models can be improved by clustering the features of both students and questions. Based on this observation, a distributed KT scheme is proposed: (1) it classifies both students and questions based on clustering technology to reduce the interaction between different feature data to improve the prediction accuracy; (2) models for different classifications are trained in parallel in this distributed deployment architecture to improve the training efficiency; (3) the combination of a students' knowledge state matrix and an RPa-LLM model is designed to display the knowledge status of students in the learning process, which can be used to build students' portraits, thus improving the interpretability of the model. Real educational data are collected to conduct experiments. The results show that the proposed scheme improves both prediction accuracy and training efficiency by 4.08% and 67.28%, respectively, compared to the baseline methods. Furthermore, the proposed method maintains the interpretability of KT models, making it suitable for practical deployment.