Integrating fine-grained attention into multi-task learning for knowledge tracing-Reference-Cited by-同舟云学术

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Integrating fine-grained attention into multi-task learning for knowledge tracing

Published:2023-07-25 Issue:5 Volume:26 Page:3347-3372
ISSN:1386-145X
Container-title:World Wide Web
language:en
Short-container-title:World Wide Web

Author:

He Liangliang,Li Xiao,Wang Pancheng,Tang Jintao,Wang Ting

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Software

Link

https://link.springer.com/content/pdf/10.1007/s11280-023-01190-y.pdf

Reference49 articles.

1. He, L.: Integrating Performance and Side Factors into Embeddings for Deep Learning-Based Knowledge Tracing, In IEEE International Conference on Multimedia and Expo, 1–6 (2021)

2. He, L., Tang, J., Li, X., Wang, P., Chen, F., Wang, T.: Multi-type factors representation learning for deep learning-based knowledge tracing, World Wide Web, 1–30 (2022)

3. Corbett, A.T., Anderson, J.R.: Knowledge tracing: Modeling the acquisition of procedural knowledge. User Model. User-Adap. Inter. 4(4), 253–278 (1994)

4. Cully, A., Demiris, Y.: Online knowledge level tracking with data-driven student models and collaborative filtering. IEEE Trans. Knowl. Data. Eng. 32(10), 2000–2013 (2019)

5. Cinquin, P.A., Guitton, P., Sauzéon, H.: Online e-learning and cognitive disabilities: A systematic review. Comput. & Educ. 130, 152–167 (2019)

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Global Feature-guided Knowledge Tracing;Proceedings of the 2024 16th International Conference on Machine Learning and Computing;2024-02-02

2. Attention and Learning Features-Enhanced Knowledge Tracing;Lecture Notes in Computer Science;2024

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