Affiliations: [a]
College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| [b]
College of Mathematics and Systems, Shandong University of Science and Technology, Qingdao, China
Correspondence:
[*]
Corresponding author. Qing-Tian Zeng. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China. E-mail: [email protected].
Abstract: With the increase in needs for personalized learning of online students, knowledge tracing (KT), a technique aimed at tracing the state of a student’s knowledge mastery and predicting performance in future exercises, has become a hot topic in personalized learning research. The behavioral features exhibited during students’ learning process bear information that impacts the state of a student’s knowledge mastery. To study the influence of learning behaviors on students’ knowledge mastery state in the learning process, we propose a Precise Modeling of Learning Process based on Multiple Behavioral Features for Knowledge Tracing model (MBFKT), which models a student’s learning process by making use of these behavioral features. MBFKT initially processes these features through multi-head attention networks, memory networks, and recurrent neural networks to model students’ learning process into three memory links: memory decline link, memory enhancement link, and memory update link. Various update strategies are designed for each memory link, and the performance of numerous possible combinations of behavioral features in the memory links is compared, for the rules of learning and forgetting to be explained. Furthermore, we also study the contribution and degree of influence of different behavioral features on a student’s knowledge mastery state, by which MBFKT is improved, thus enhancing the accuracy of prediction. Through experiments on real online education datasets and comparison with existing benchmark methods, it is observed that MBFKT has evident advantages in predicting performance with good interpretability.
