Optimization measures for students’ autonomous learning based on deep learning and human-computer interaction technology

Article type: Research Article

Authors: Chen, Yi^a | Lin, Xiaopin^b | Li, Zheng^{c; *}

Affiliations: [a] School of Economics and Management, Hubei Polytechnic University, Huangshi, Hubei, China | [b] Zhejiang Province Hangzhou No.14 High School, Hangzhou, Zhejiang, China | [c] College of Finance and Economics, Shanghai Lida University, Shanghai, China

Correspondence: [*] Corresponding author: Zheng Li, College of Finance and Economics, Shanghai Lida University, Shanghai 200000, China. E-mail: [email protected].

Abstract: In order to effectively improve students’ learning outcomes and teachers’ teaching quality, this paper explores an optimization measure for students’ autonomous learning based on deep learning and Human-Computer Interaction (HCI) technology. Our proposed optimization measure constructs an interactive micro-video teaching model from teaching resources, teaching process, and teaching evaluation perspectives. The experimental results demonstrate that our proposed optimization measure can effectively improve students’ learning outcomes and satisfaction while enhancing their autonomous learning abilities and learning motivations.

Keywords: Micro-video design, human-computer interaction, teaching experiment, autonomous learning ability, knowledge test

DOI: 10.3233/JCM-247554

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 3079-3091, 2024