Empirical Analysis of Influencing Factors of Desktop Virtual Experiment Teaching Platforms Based on Deep Learning

Jiahua Zhu; Fangna Chen; Wanbing Ma; Xiaolei Wang; Nini Cheng

doi:10.6918/IJOSSER.202412_7(12).0062

Authors

Jiahua Zhu
Fangna Chen
Wanbing Ma
Xiaolei Wang
Nini Cheng

DOI:

https://doi.org/10.6918/IJOSSER.202412_7(12).0062

Keywords:

Virtual experiment; Deep learning; Influence factors; Correlation analysis; Component analysis.

Abstract

The virtual experiment teaching platform meets the needs of information-based learning which is personalized, fragmented, and all-day-long. At the same time, it emphasizes situativity, interactivity, immersion, and behavioral reality and thereby can effectively promote the occurrence and maintenance of deep learning among students. The research analyzed the influencing factors of deep learning based on desktop virtual experiment teaching platforms. Based on the literature research and questionnaire survey, the theoretical model of the influence factors was constructed. The data were processed through the component analysis, correlation analysis, multiple regression analysis, and other quantitative methods. The results show that the influencing factors of deep learning by virtual experiment teaching platforms are situational verisimilitude, modes of knowledge presentation, overall interface settings, software feedback, operational credibility, operational guidance settings, self-inquiry space, and so on.

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