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Research on Integrating Forgetting Behavior into Student Models for Online Learning Systems

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DOI: 10.23977/jaip.2024.070122 | Downloads: 16 | Views: 151


Ze Song 1


1 University of Science and Technology of China, Hefei, Anhui, 230026, China

Corresponding Author

Ze Song


Modelling students accurately is an important task in online learning systems. In online learning systems, student models are usually built by dealing with students’ historical data of answering questions as input, and eventually output to what extent the student has mastered a certain knowledge component. To evaluate a student model, a commonly used method, namely knowledge tracing, is to build multiple student models based on multiple continuous historical data as mentioned above, then predict whether or not a student can answer a question correctly, and finally compare predicted results with true results. However, the behavior of students is complicated and unpredictable, thus makes student modelling and knowledge tracing become very difficult tasks. Based on existing research of knowledge tracing, especially deep knowledge tracing which uses LSTM to model students, this paper proposes a novel method of student modeling. Compared with other state-of-the-art student modeling methods, the most significant feature of our modeling methods is our method can take students' forgetting behavior into consideration. Moreover, our modeling method can appropriately handle situations that one question corresponds to multiple knowledge components. To test the performance of our student model, this paper applies our student model to the knowledge tracing task. Based on our experiments in public datasets, when one question corresponds to multiple knowledge components and the length of students' historical data is greater, our model performs better in terms of all metrics compared to state-of-the-art knowledge tracing methods.


Online Learning Systems, Student Modelling, Knowledge Tracing, Forgetting Behaviour, LSTM


Ze Song, Research on Integrating Forgetting Behavior into Student Models for Online Learning Systems. Journal of Artificial Intelligence Practice (2024) Vol. 7: 146-153. DOI:


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