Modeling Portraits of Students and Exercises for Exercise Recommendation
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-4752-2_19
Reference21 articles.
1. Nabizadeh, A., Leal, J., Rafsanjani, H., Shah, R.: Learning path personalization and recommendation methods: a survey of the state-of-the-art. Expert Syst. Appl. 159, 113596 (2020)
2. Zhang, Q., Lu, J., Zhang, G.: Recommender systems in E-learning. J. Smart Environ. Green Comput. 1, 76–89 (2021)
3. Ma, H., Huang, Z., Tang, W., Zhang, X.: Exercise recommendation based on cognitive diagnosis and neutrosophic set. In: 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 1467–1472. IEEE (2022)
4. Huang, S., Liu, Q., Chen, J., Hu, X., Liu, Z., Luo, W.: A design of a simple yet effective exercise recommendation system in K-12 online learning. In: Rodrigo, M.M., Matsuda, N., Cristea, A.I., Dimitrova, V. (eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. AIED 2022. Lecture Notes in Computer Science, vol 13356. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-11647-6_36
5. Li, Z., et al.: Exercise recommendation algorithm based on improved collaborative filtering. In: 21st 2021 International Conference on Advanced Learning Technologies (ICALT), pp. 47–49. IEEE (2021)
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