Article
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Research on Personalized Teaching Strategies Selection based on Deep Learning
Version 1
: Received: 25 August 2024 / Approved: 26 August 2024 / Online: 27 August 2024 (00:34:19 CEST)
How to cite: Wang, C.; Chen, J.; Xie, Z.; Zou, J. Research on Personalized Teaching Strategies Selection based on Deep Learning. Preprints 2024, 2024081887. https://doi.org/10.20944/preprints202408.1887.v1 Wang, C.; Chen, J.; Xie, Z.; Zou, J. Research on Personalized Teaching Strategies Selection based on Deep Learning. Preprints 2024, 2024081887. https://doi.org/10.20944/preprints202408.1887.v1
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.
Keywords
Personalized Teaching; Strategies Selection; Deep Learning; Individual Development
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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