Deep Knowledge Tracing Model Integrating Learning Process and Behavior
The purpose of knowledge tracking is to continuously assess the state of students ′ knowledge and to predict their future learning performance. The student learning process is essentially an interaction between students, knowledge points, and exercises, and students influence the learning process th...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | Chinese |
| Published: |
Harbin University of Science and Technology Publications
2024-10-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: | |
| Online Access: | https://hlgxb.hrbust.edu.cn/#/digest?ArticleID=2363 |
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| Summary: | The purpose of knowledge tracking is to continuously assess the state of students ′ knowledge and to predict their future learning performance. The student learning process is essentially an interaction between students, knowledge points, and exercises, and students influence the learning process through their learning behaviors. Learning behaviors include knowledge acquisition behaviors and knowledge forgetting behaviors. In order to accurately model the learning process and learning behavior in knowledge tracking, a deep knowledge tracking model that integrates the learning process and learning behavior is proposed. The model combines item response theory and LSTM to model the interactions between knowledge points and exercises, uses a monotonic attention mechanism to fit students ′ learning behaviors, and defines two decoders to capture the interactions between students and knowledge points and exercises, thus fusing learning processes and learning behaviors. Experimental results on the real datasets ASSISTment2009 and ASSISTment2017 show that the model outperforms existing knowledge tracking models, and the prediction accuracy of the model is improved by 1% on both datasets compared to the suboptimal model. |
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| ISSN: | 1007-2683 |