Research on Personalized Teaching Strategies Selection based on Deep Learning

Chun Wang; Jiexiao Chen; Ziyang Xie; Jianke Zou

doi:10.2991/978-94-6463-560-7_14

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Research on Personalized Teaching Strategies Selection based on Deep Learning

Authors

Chun Wang¹^{, *}, Jiexiao Chen², Ziyang Xie³, Jianke Zou⁴

¹LaFetra College of Education, University of La Verne, La Verne, CA, 91750, USA

²School of Culture, Education, and Human Development, New York University, New York, 10003, USA

³Department of Art and Design, Hunan University of Humanities, Science and Technology, Hunan, 417000, China

⁴HSBC Business School, Peking University, Peking, 100091, China

^*Corresponding author.

Corresponding Author

Chun Wang

Available Online 1 November 2024.

DOI: 10.2991/978-94-6463-560-7_14 How to use a DOI?
Keywords: Personalized Teaching; Strategies Selection; Deep Learning; Individual Development
Abstract: Traditional classroom teaching model can no longer meet the needs of students’ ability development. As a new model actively advocated in the modern education concept, personalized teaching focuses on the development needs of students and emphasizes respect for students’ individualized growth. In this work, we utilize deep learning method to recommend teaching strategies for different features of students. Specifically, the proposed method includes the construction of a multi-layer neural network model, which can analyze students’ multi-dimensional learning data and behavior patterns, so as to recommend the most suitable teaching strategies for individual students. Through data collection and preprocessing, model architecture design, model training and optimization, the model gradually improves the accuracy of prediction and recommendation. Experimental analysis verifies the effectiveness of the model through field teaching experiments, and the results show that the personalized teaching strategy based on deep learning can significantly improve the learning effect and participation of students.
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 4th International Conference on Internet Technology and Educational Informatization (ITEI 2024)
Series: Atlantis Highlights in Social Sciences, Education and Humanities
Publication Date: 1 November 2024
ISBN: 978-94-6463-560-7
ISSN: 2667-128X
DOI: 10.2991/978-94-6463-560-7_14 How to use a DOI?
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

ris enw bib

TY  - CONF
AU  - Chun Wang
AU  - Jiexiao Chen
AU  - Ziyang Xie
AU  - Jianke Zou
PY  - 2024
DA  - 2024/11/01
TI  - Research on Personalized Teaching Strategies Selection based on Deep Learning
BT  - Proceedings of the 2024 4th International Conference on Internet Technology and Educational Informatization (ITEI 2024)
PB  - Atlantis Press
SP  - 110
EP  - 116
SN  - 2667-128X
UR  - https://doi.org/10.2991/978-94-6463-560-7_14
DO  - 10.2991/978-94-6463-560-7_14
ID  - Wang2024
ER  -

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