Determinants of College Students’ Online Fragmented Learning Effect: An Analysis of Teaching Courses on Scientific Research Software on the Bilibili Platform

Zhigang Li and Yalin Yang ()
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Zhigang Li: College of Management Science, Chengdu University of Technology, Chengdu 610059, China
Yalin Yang: College of Management Science, Chengdu University of Technology, Chengdu 610059, China

Sustainability, 2023, vol. 15, issue 22, 1-16

Abstract: In the era of mobile Internet, online fragmented learning has become one of the mainstream ways for college students to study independently. Analyzing the online fragmented learning effect (OFLE) has essential reference value for guiding the online learning behavior of college students and optimizing the allocation of online learning platform resources. It also provides sustainable solutions for enhancing college students’ online independent learning effect. For this paper, we took college students who have studied teaching courses on scientific research software (TCSRS) on Bilibili as our research subjects, constructed a theoretical model of college students’ OFLE based on the theory of online learning and fragmented learning, verified the model through using questionnaire data, and analyzed the determinants of college students’ OFLE and its mechanisms. Our results indicate the following: (1) Most college students spend a dispersed amount of time learning TCSRS, showing the characteristics of “fragmentation”. (2) Learning motivation (LM), self-efficacy (SE), and fragmented time utilization (FTU) have a significant positive effect on college students’ OFLE. (3) Knowledge fragmentation (KF) affects college students’ OFLE, but it is not significant. These dimensions provide a theoretical reference for quantitative research on the impact of college students’ OFLE. Finally, also in this paper, improvement countermeasures are proposed from the perspective of improving college students’ OFLE and ability.

Keywords: online fragmented learning; learning effect analytics; fragmented time utilization; knowledge fragmentation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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