SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing
Knowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7704 |
| _version_ | 1797592136751775744 |
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| author | Qi Song Wenjie Luo |
| author_facet | Qi Song Wenjie Luo |
| author_sort | Qi Song |
| collection | DOAJ |
| description | Knowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere with the learning process. Prior research has employed diverse techniques to address the issue of interference caused by forgetting factors in predictions, yet many of these methods fail to fully leverage the forgetting information contained within learning records. This paper proposes a synthetically forgetting behavior knowledge tracing (SFBKT) model that comprehensively models a student’s knowledge level by considering both individual forgetting factors and group status. Specifically, the model initially extracts forgetting information from exercise records in the input module, then updates the student’s knowledge state through an improved continuous-time long short-term memory network (CTLSTM), and finally combines the individual state with the group state using collaborative filtering to predict the student’s ability to correctly answer the next exercise. Our predictive model has been evaluated using four public education datasets. The experimental results indicate that our model’s predictions are effective and outperform other existing methods. |
| first_indexed | 2024-03-11T01:47:11Z |
| format | Article |
| id | doaj.art-8852a75a784f41998234a7273ddd3caa |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:11Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8852a75a784f41998234a7273ddd3caa2023-11-18T16:10:39ZengMDPI AGApplied Sciences2076-34172023-06-011313770410.3390/app13137704SFBKT: A Synthetically Forgetting Behavior Method for Knowledge TracingQi Song0Wenjie Luo1School of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaSchool of Cybersecurity and Computer, Hebei University, Baoding 071002, ChinaKnowledge tracing (KT) aims to model students’ knowledge levels based on their historical learning records and predict their future learning performance, which constitutes an essential component of intelligent education. Learning and forgetting are closely related, and forgetting can often interfere with the learning process. Prior research has employed diverse techniques to address the issue of interference caused by forgetting factors in predictions, yet many of these methods fail to fully leverage the forgetting information contained within learning records. This paper proposes a synthetically forgetting behavior knowledge tracing (SFBKT) model that comprehensively models a student’s knowledge level by considering both individual forgetting factors and group status. Specifically, the model initially extracts forgetting information from exercise records in the input module, then updates the student’s knowledge state through an improved continuous-time long short-term memory network (CTLSTM), and finally combines the individual state with the group state using collaborative filtering to predict the student’s ability to correctly answer the next exercise. Our predictive model has been evaluated using four public education datasets. The experimental results indicate that our model’s predictions are effective and outperform other existing methods.https://www.mdpi.com/2076-3417/13/13/7704knowledge tracingforgetting behaviorcontinuous-time LSTMdeep learningintelligence educationcollaborative filtering |
| spellingShingle | Qi Song Wenjie Luo SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing Applied Sciences knowledge tracing forgetting behavior continuous-time LSTM deep learning intelligence education collaborative filtering |
| title | SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing |
| title_full | SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing |
| title_fullStr | SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing |
| title_full_unstemmed | SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing |
| title_short | SFBKT: A Synthetically Forgetting Behavior Method for Knowledge Tracing |
| title_sort | sfbkt a synthetically forgetting behavior method for knowledge tracing |
| topic | knowledge tracing forgetting behavior continuous-time LSTM deep learning intelligence education collaborative filtering |
| url | https://www.mdpi.com/2076-3417/13/13/7704 |
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