A Deep Learning Knowledge Tracing Model Based on Attention Mechanism
In this paper we proposed a knowledge tracing method based on Transformer structure, improved the embedded representation of interactive records, designed a gate unit suitable for this model, and optimized the input processing of self-attention sublayer to improve the predictive performance of deep...
| 出版年: | Taiyuan Ligong Daxue xuebao |
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| 主要な著者: | , , , |
| フォーマット: | 論文 |
| 言語: | 英語 |
| 出版事項: |
Editorial Office of Journal of Taiyuan University of Technology
2021-07-01
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| 主題: | |
| オンライン･アクセス: | https://tyutjournal.tyut.edu.cn/englishpaper/show-410.html |
| 要約: | In this paper we proposed a knowledge tracing method based on Transformer structure, improved the embedded representation of interactive records, designed a gate unit suitable for this model, and optimized the input processing of self-attention sublayer to improve the predictive performance of deep knowledge tracing model. The experimental results on four commonly used public data sets show that compared with previous methods, the model proposed in this paper can better reflect learners’ mastery of knowledge points, and has better performance on data sets with large sample sizes. |
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| ISSN: | 1007-9432 |