An XGBoost-Based Knowledge Tracing Model
Abstract The knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different stude...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2023-02-01
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| Series: | International Journal of Computational Intelligence Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44196-023-00192-y |
| _version_ | 1811165739136057344 |
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| author | Wei Su Fan Jiang Chunyan Shi Dongqing Wu Lei Liu Shihua Li Yongna Yuan Juntai Shi |
| author_facet | Wei Su Fan Jiang Chunyan Shi Dongqing Wu Lei Liu Shihua Li Yongna Yuan Juntai Shi |
| author_sort | Wei Su |
| collection | DOAJ |
| description | Abstract The knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different students. However, the current knowledge tracing models still have problems of inaccurate prediction results and poor features utilization. The study applies XGBoost algorithm to knowledge tracing model to improve the prediction performance. In addition, the model also effectively handles the multi-skill problem in the knowledge tracing model by adding the features of problem and knowledge skills. Experimental results show that the best AUC value of the XGBoost-based knowledge tracing model can reach 0.9855 using multiple features. Furthermore, compared with previous knowledge tracing models used deep learning, the model saves more training time. |
| first_indexed | 2024-04-10T15:42:34Z |
| format | Article |
| id | doaj.art-eab8a42c0aee4129863ec10e8d7f350c |
| institution | Directory Open Access Journal |
| issn | 1875-6883 |
| language | English |
| last_indexed | 2024-04-10T15:42:34Z |
| publishDate | 2023-02-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj.art-eab8a42c0aee4129863ec10e8d7f350c2023-02-12T12:22:17ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832023-02-011611910.1007/s44196-023-00192-yAn XGBoost-Based Knowledge Tracing ModelWei Su0Fan Jiang1Chunyan Shi2Dongqing Wu3Lei Liu4Shihua Li5Yongna Yuan6Juntai Shi7School of Information Science and Engineering, Lanzhou UniversityCITIC Bank Software Development CenterDuzhe Publishing Group Co. Ltd.Yizhichuan Primary SchoolDuzhe Publishing Group Co. Ltd.School of Information Science and Engineering, Lanzhou UniversitySchool of Information Science and Engineering, Lanzhou UniversitySchool of Information Science and Engineering, Lanzhou UniversityAbstract The knowledge tracing (KT) model is an effective means to realize the personalization of online education using artificial intelligence methods. It can accurately evaluate the learning state of students and conduct personalized instruction according to the characteristics of different students. However, the current knowledge tracing models still have problems of inaccurate prediction results and poor features utilization. The study applies XGBoost algorithm to knowledge tracing model to improve the prediction performance. In addition, the model also effectively handles the multi-skill problem in the knowledge tracing model by adding the features of problem and knowledge skills. Experimental results show that the best AUC value of the XGBoost-based knowledge tracing model can reach 0.9855 using multiple features. Furthermore, compared with previous knowledge tracing models used deep learning, the model saves more training time.https://doi.org/10.1007/s44196-023-00192-yKnowledge tracingDeep learningMulti-skillXGBoost |
| spellingShingle | Wei Su Fan Jiang Chunyan Shi Dongqing Wu Lei Liu Shihua Li Yongna Yuan Juntai Shi An XGBoost-Based Knowledge Tracing Model International Journal of Computational Intelligence Systems Knowledge tracing Deep learning Multi-skill XGBoost |
| title | An XGBoost-Based Knowledge Tracing Model |
| title_full | An XGBoost-Based Knowledge Tracing Model |
| title_fullStr | An XGBoost-Based Knowledge Tracing Model |
| title_full_unstemmed | An XGBoost-Based Knowledge Tracing Model |
| title_short | An XGBoost-Based Knowledge Tracing Model |
| title_sort | xgboost based knowledge tracing model |
| topic | Knowledge tracing Deep learning Multi-skill XGBoost |
| url | https://doi.org/10.1007/s44196-023-00192-y |
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