Motivation of Students’ Persistency for Online Learning under Multiple Mediation Effect

Authors

  • Yan Wang
  • Peng Su
  • Xiwen Liu
  • Xin Zhao
  • Fengming Jiao
  • Guiling Liu
  • Changtian Wang

DOI:

https://doi.org/10.3991/ijet.v17i07.30399

Keywords:

support vector machine (SVM), multiple mediation effect, online learning, learning persistency, motivation, willingness

Abstract


This paper probed deep into the motivation of students’ persistency for online learning from the perspective of user experience of online learning platforms, in the purpose of increasing user stickiness and formulating effective operation strategies in a targeted manner. Existing studies on the motivation of students’ persistency for online learning mostly focus on theories, while few of them have talked about the problem with the multiple mediation effect taken into consideration, for this reason, this paper aims to fill in this research gap and explore the mechanism behind the motivation of students to carry out online learning persistently under the multiple mediation effect. At first, this paper built an improved support vector machine (SVM) classifier and used it to predict the duration of students' online learning; then, it adopted a structural equation model to analyze the data of students’ willingness to continue online learning; after that, this paper gave a theoretical analysis on the motivation of students’ persistency for online learning under multiple mediation effect, and constructed a basic regression model for the said matter; at last, this paper employed experimental results to verify the prediction accuracy of the constructed model, and gave the corresponding estimation results.

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Published

2022-04-12

How to Cite

Wang, Y. ., Su, P. ., Liu, X. ., Zhao, X. ., Jiao , F. ., Liu, G. ., & Wang, C. . (2022). Motivation of Students’ Persistency for Online Learning under Multiple Mediation Effect. International Journal of Emerging Technologies in Learning (iJET), 17(07), pp. 260–274. https://doi.org/10.3991/ijet.v17i07.30399

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Papers