Applied Mathematics and Nonlinear Sciences (Jan 2024)

Research on the Evaluation Method of Online Learning Satisfaction of College Students Based on Hierarchical Analysis Approach

  • Ji Yiping,
  • Zhernovnykova Oksana

DOI
https://doi.org/10.2478/amns.2023.2.01552
Journal volume & issue
Vol. 9, no. 1

Abstract

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This paper adopts the hierarchical analysis method as the weight calculation method for the evaluation of online learning satisfaction, calculates the weights between the evaluation indexes, and constructs the evaluation system of college students’ online learning satisfaction. Combining the teaching experience, the characteristics of online teaching and the items recorded in the web logs in the online teaching platform, the judgment matrix for online learning satisfaction evaluation is established, and the reasonableness of the matrix is judged by the consistency checking method. Evaluation standards are formulated based on the online learning evaluation information, and the research on online learning satisfaction evaluation is designed, while the online learning satisfaction evaluation of college students is analyzed by using the method of statistical analysis. The results show that the comprehensive learning evaluation scores derived from the use of hierarchical analysis are significantly more objective than the traditional evaluation of students’ learning effectiveness using only the end-of-term test scores, with a score difference of 15, and more than 85.24% of the students based on the hierarchical analysis method of college students’ online learning satisfaction evaluation method, which is effective in promoting the students’ regular participation in online learning. The correlation coefficients of online learning experience with clarity of teaching goals and expectations, teacher-student communication and exchange, learning support services, learning motivation, learning hardware equipment and operability, and teaching methods are 0.491, 0.524, 0.435, 0.596, 0.542, 0.391, respectively, which are all significant at 1% level, i.e., only by guaranteeing a higher level of learning effect can we improve students’ satisfaction with online learning.

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