| An XGBoost-Based Knowledge Tracing Model | |
Su, W(苏伟)1,2 ; Jiang, Fan3; Shi, Chunyan4; Wu, Dongqing5; Liu, Lei4; Li, Shihua1; Yuan, YN(袁永娜)1; Shi, Juntai1 | |
| 2023-12-01 | |
| Source Publication | International Journal of Computational Intelligence Systems Impact Factor & Quartile |
| ISSN | 1875-6891 |
| Volume | 16Issue:1 |
| 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. © 2023, The Author(s). |
| Keyword | Deep learning E-learning Education computing Learning systems 'current Artificial intelligence methods Deep learning Knowledge tracings Multi skills On-line education Personalizations Personalized instruction Tracing model Xgboost |
| Publisher | Springer Science and Business Media B.V. |
| DOI | 10.1007/s44196-023-00192-y |
| Indexed By | EI |
| Language | 英语 |
| EI Accession Number | 20230813603999 |
| EI Keywords | Students |
| EI Classification Number | 461.4 Ergonomics and Human Factors Engineering |
| Original Document Type | Journal article (JA) |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://ir.lzu.edu.cn/handle/262010/500814 |
| Collection | 信息科学与工程学院 |
| Corresponding Author | Su, Wei |
| Affiliation | 1.School of Information Science and Engineering, Lanzhou University, Gansu, Lanzhou, China; 2.Key Laboratory of Media Convergence Technology and Communication, Gansu, Lanzhou, China; 3.CITIC Bank Software Development Center, Beijing, China; 4.Duzhe Publishing Group Co. Ltd., Gansu, Lanzhou, China; 5.Yizhichuan Primary School, Gansu, Lanzhou, China |
| First Author Affilication | School of Information Science and Engineering |
| Corresponding Author Affilication | School of Information Science and Engineering |
| Recommended Citation GB/T 7714 | Su, Wei,Jiang, Fan,Shi, Chunyan,et al. An XGBoost-Based Knowledge Tracing Model[J]. International Journal of Computational Intelligence Systems,2023,16(1). |
| APA | Su, Wei.,Jiang, Fan.,Shi, Chunyan.,Wu, Dongqing.,Liu, Lei.,...&Shi, Juntai.(2023).An XGBoost-Based Knowledge Tracing Model.International Journal of Computational Intelligence Systems,16(1). |
| MLA | Su, Wei,et al."An XGBoost-Based Knowledge Tracing Model".International Journal of Computational Intelligence Systems 16.1(2023). |
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