Mou Chuan, Tian Yi, Zhang Fengrui, Zhu Chao
Department of Sport and Exercise Sciences, Kunsan National University, Gunsan, South Korea.
King's Business School, King's College London, London, United Kingdom.
Front Psychol. 2022 Jan 17;12:766621. doi: 10.3389/fpsyg.2021.766621. eCollection 2021.
This study aims to explore the current situation and strategy formulation of sports psychology teaching in colleges and universities following adaptive learning and deep learning under information education. The informatization in physical education, teaching methods, and teaching processes make psychological education more scientific and efficient. First, the relevant theories of adaptive learning and deep learning are introduced, and an adaptive learning analysis model is implemented. Second, based on the deep learning automatic encoder, college students' sports psychology is investigated and the test results are predicted. Finally, the current situation and development strategy of physical education in colleges and universities are analyzed. The results show that when the learning rate is 1, 0.1, and 0.01, there is no significant change in the analysis factors of recall, ndcg, item_coverage, and sps. When the learning rate is 1, their analysis factors change obviously, and it is calculated that the optimal learning rate of the model is 1. And the difficulty of the recommended test questions by using the sports psychology teaching method based on adaptive learning and deep learning is relatively stable. The test questions include various language points of sports psychology. Compared with others methods, adaptive learning and deep learning can provide comprehensive test questions for sports psychology teaching. This study provides technical support for the reform of sports psychology teaching in colleges and universities and contributes to optimizing the information-based teaching mode.
本研究旨在探讨信息教育背景下，高校体育心理学教学在适应性学习和深度学习后的现状及策略制定。体育教育、教学方法和教学过程的信息化使心理教育更加科学高效。首先，介绍适应性学习和深度学习的相关理论，并实施一个适应性学习分析模型。其次，基于深度学习自动编码器，对大学生的体育心理学进行调查并预测测试结果。最后，分析高校体育教育的现状和发展策略。结果表明，当学习率为1、0.1和0.01时，召回率、归一化折损累计增益、项目覆盖率和标准化精准率的分析因素无显著变化。当学习率为1时，其分析因素变化明显，计算得出该模型的最优学习率为1。并且，基于适应性学习和深度学习的体育心理学教学方法所推荐测试题目的难度相对稳定。测试题涵盖体育心理学的各种语言点。与其他方法相比，适应性学习和深度学习可为体育心理学教学提供全面的测试题。本研究为高校体育心理学教学改革提供技术支持，有助于优化信息化教学模式。
