Open AccessInfluence of Self-efficacy Improvement on Online Learning Participation
Author(s) -
Geng Lu
Publication year - 2022
Publication title -
international journal of emerging technologies in learning/international journal: emerging technologies in learning
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.454
H-Index - 24
eISSN - 1868-8799
pISSN - 1863-0383
DOI - 10.3991/ijet.v17i01.28719
Subject(s) - online learning , computer science , inefficiency , the internet , cluster analysis , entropy (arrow of time) , self efficacy , online participation , artificial intelligence , machine learning , psychology , multimedia , world wide web , social psychology , physics , quantum mechanics , economics , microeconomics
More and more online learning apps are emerging, thanks to the development of Internet plus education and online learning platforms. Learning efficacy is the leading impactor of online learning participation. To avoid inefficiency and poor effect of online learning, it is necessary to explore the theory on the relationship between self-efficacy improvement and online learning participation. This paper examines the influence of self-efficacy improvement on online learning participation. Firstly, a general normal distribution map was drawn for self-efficacy. Then, a prediction model was established for participation based on the series of online learning behaviors. In addition, the k-means clustering (KMC) algorithm was optimized by information entropy, and the flow of the improved KMC was explained. The proposed model was proved valid through experiments.