MPHINE-KT: Heterogeneous Information Network Embedding With Metapath Knowledge Tracing

Abstract

Contributions: This article proposes a new knowledge tracing (KT) model called heterogeneous information network embedding with metapath KT (MPHINE-KT) to model the knowledge state of learners and predict the accuracy of learners' future academic performance. To mine the transfer relationships between different knowledge points in sequences, the learning experience of learners is designed. The effects of individuality and commonality between learners' learning experience and abilities through heterogeneous information networks are analyzed, learners' individual attributes are introduced, and the problem of data sparsity is alleviated. The design improves the prediction effect of KT models on learners' future performance.Background: Online education platforms use KT models to analyze learners' online study. These models assess and predict learning effects, providing multidimensional feedback and guidance. They fail to adequately extract the features of the transfer relationships between different knowledge points for learners. Moreover, they lack discrimination regarding the differences in the characteristics of individual attributes of learners, and does not fully integrate the multiattribute features of learners, resulting in the sparsity of input data. This restricts the KT model's ability to predict learners' performance in answering future exercises.Intended Outcomes: The MPHINE-KT model analyzes the historical interaction sequence data of students and exercise problems, fully explores the relationships among learners, learning experience, and learning abilities, and more accurately predicts whether learners can answer new exercise problems correctly in the future, thus providing support for key educational aspects such as personalized education.Application Design: The MPHINE-KT model first designs a learner's learning experience recognition module based on a learner behavior graph for capturing learners' complex representations in the learning process, and then constructs a learner heterogeneous information network. Finally, as an additional input, the learner's feature vector is put into the gated recurrent unit (GRU) temporal prediction model, so as to improve the accuracy of predicting learners' future learning performance.Findings: Experiments are conducted on several real-world datasets, and the experimental results prove that the proposed model has a better performance compared to existing popular KT models.