Proceedings of the 2024 2nd International Conference on Language, Innovative Education and Cultural Communication (CLEC 2024)

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Cross-Course Learner Modeling Based on Deep Cognitive Diagnosis

Authors
Ying Yuan1, *, Mengmeng Sheng1, Jing Zhang1
1Department of Computer and Information Security, Zhejiang Police College, Hangzhou, China
*Corresponding author. Email: yuanying@zjjcxy.cn
Corresponding Author
Ying Yuan
Available Online 3 July 2024.
DOI
10.2991/978-2-38476-263-7_53How to use a DOI?
Keywords
deep learning; cognitive diagnosis; cross-domain recommendation; cross-course learner modeling
Abstract

With the rapid development of online education and the advent of the big data era, how to accurately recommend learning resources and effectively diagnose learners' cognitive states has become an urgent issue to be addressed in the field of education. Cognitive diagnosis and cross-domain recommendation are both very important applications in education, they can help teachers better understand students' learning states and capabilities, and also assist students in finding learning resources and methods that are more suitable for them. This paper aims to research cross-course learner modeling based on deep cognitive diagnosis to enhance learners' cognitive levels and knowledge transfer abilities. By combining deep learning models with cognitive diagnostic techniques, it is possible to accurately assess and predict learners' capabilities, and through cross-domain recommendation technology, achieve personalized cross-course knowledge transfer to evaluate learners' cognitive states and learning outcomes in new courses. This study has designed a series of experiments to verify the effectiveness of this method and to analyze and discuss the results. The experimental outcomes indicate that cross-course learner modeling based on deep cognitive diagnosis can significantly improve learners' cognitive levels and knowledge transfer abilities, providing beneficial insights for personalized learning and intelligent education in the educational field.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 2nd International Conference on Language, Innovative Education and Cultural Communication (CLEC 2024)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
3 July 2024
ISBN
10.2991/978-2-38476-263-7_53
ISSN
2352-5398
DOI
10.2991/978-2-38476-263-7_53How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ying Yuan
AU  - Mengmeng Sheng
AU  - Jing Zhang
PY  - 2024
DA  - 2024/07/03
TI  - Cross-Course Learner Modeling Based on Deep Cognitive Diagnosis
BT  - Proceedings of the 2024 2nd International Conference on Language, Innovative Education and Cultural Communication (CLEC 2024)
PB  - Atlantis Press
SP  - 428
EP  - 435
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-263-7_53
DO  - 10.2991/978-2-38476-263-7_53
ID  - Yuan2024
ER  -