Toward a comprehensive understanding of massive open online course adoption among college students

Abstract This paper investigated the factors affecting the behavioral intention (BI) and usage behavior (UB) of 548 Chinese college students in massive open online courses (MOOCs). We extended the unified theory of acceptance and use of technology model by incorporating self‐regulated learning (SRL) management, perceived pleasure (PP), and perceived cost constructs. This study also analyzed the moderating role of individual differences in gender, grade, major, and experience in MOOCs. Data were collected through both offline surveys across 10 universities and an online survey platform. Results from structural equation modeling and multi‐linear regression analyses revealed that students' BI was significantly and positively influenced by six factors: performance expectancy, effort expectancy, facilitating conditions (FC), social influence, SRL management (SM), and PP. Furthermore, we found that UB was predicted by BI, FC, and SM. Moreover, BI played a mediating role in the relationship between the two determining variables (i.e., FC and SM) and UB. Differences in MOOC experience level had a moderation effect. The study provided implications for various stakeholders regarding designing MOOC platforms, structures, and teaching activities.

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Publication Details

Journal
Future in Educational Research
Published
2024-10-18
DOI
https://doi.org/10.1002/fer3.64
Citations
1
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
0.41
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article

Toward a comprehensive understanding of massive open online course adoption among college students

Shan Li, Yuxia Du, Qiaoli Huang
1 citations
Future in Educational Research
Online Learning and Analytics
0.41
article

Toward a comprehensive understanding of massive open online course adoption among college students

Shan Li, Yuxia Du, Qiaoli Huang
article en
1 citations

Abstract

Abstract This paper investigated the factors affecting the behavioral intention (BI) and usage behavior (UB) of 548 Chinese college students in massive open online courses (MOOCs). We extended the unified theory of acceptance and use of technology model by incorporating self‐regulated learning (SRL) management, perceived pleasure (PP), and perceived cost constructs. This study also analyzed the moderating role of individual differences in gender, grade, major, and experience in MOOCs. Data were collected through both offline surveys across 10 universities and an online survey platform. Results from structural equation modeling and multi‐linear regression analyses revealed that students' BI was significantly and positively influenced by six factors: performance expectancy, effort expectancy, facilitating conditions (FC), social influence, SRL management (SM), and PP. Furthermore, we found that UB was predicted by BI, FC, and SM. Moreover, BI played a mediating role in the relationship between the two determining variables (i.e., FC and SM) and UB. Differences in MOOC experience level had a moderation effect. The study provided implications for various stakeholders regarding designing MOOC platforms, structures, and teaching activities.

Future in Educational ResearchVol. 3(2)
Lehigh University (US), Guangzhou University (CN)
Quality Education
Openalex Percentile: Top 26%
Online Learning and Analytics
0.41
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