| Literature DB >> 35668899 |
Yuk Ming Tang1,2, Yui-Yip Lau3, Ka Yin Chau2.
Abstract
The outbreak of the COVID-19 pandemic has fundamentally shifted learning from the traditional classroom approach to online learning. As such, this study used a revision centre as a case study to develop the factors that contribute to the theoretical framework of online peer learning in the higher education sector due to COVID-19. This study also explores the integrated effects of online peer learning on students and investigates whether advanced information technology creates new opportunities or additional burdens for students in adopting online peer learning environments. Descriptive statistical analysis, factor analysis, and correlation analysis were conducted on survey data gathered from 204 sub-degree students in Hong Kong. The results addressed four main factors developed from 39 variables: enhancement of learning ability, the attitude toward learning, motivation for learning, and interpersonal relationship which were closely associated. The study findings provide strategies and constructive recommendations for educators to develop a new online learning pedagogy, construct sustainable online peer learning, and effectively manage students' online learning to meet the needs for post-COVID online education.Entities:
Keywords: Higher education; Online learning; Peer learning; Post-COVID; Revision centre; Student perspectives
Year: 2022 PMID: 35668899 PMCID: PMC9157034 DOI: 10.1007/s10639-022-11136-y
Source DB: PubMed Journal: Educ Inf Technol (Dordr) ISSN: 1360-2357
Fig. 1Number of confirmed cases of COVID-19, as of 30 September 2021. (World Health Organization, 2021)
Fig. 2Number of deaths of COVID-19, as of 30 September 2021. (World Health Organization, 2021)
Fig. 3Total duration of school closures, as of 30 September 2021 (UNESCO, 2021)
KMO and bartlett test
| The number of valid KMO sampling | 0.958 | |
| Bartlett sphericity tests | Similar Chi-square | 7730.884 |
| Degree of freedom | 741** |
**P < 0.01
Explanation of total variance
| Component | Eigenvalue | Initial Eigenvalue | Accumulation (%) | Total | Loading Squares Extracted | Accumulation (%) |
|---|---|---|---|---|---|---|
| 1 | 22.304 | 57.189 | 57.189 | 22.304 | 57.189 | 57.189 |
| 2 | 2.170 | 5.564 | 62.754 | 2.170 | 5.564 | 62.754 |
| 3 | 1.432 | 3.673 | 66.426 | 1.432 | 3.673 | 66.426 |
| 4 | 1.170 | 3.001 | 69.427 | 1.170 | 3.001 | 69.427 |
Fig. 4Screen plot of the factor analysis
Reliability analysis
| Factors | Mean (SD) | Consistency reliability |
|---|---|---|
| Overall | 0.932 | |
| ELA | 4.02 (0.661) | 0.882 |
| IR | 3.80 (0.744) | 0.843 |
| AL | 4.01 (0.677) | 0.869 |
| ML | 3.84 (0.738) | 0.779 |
Construct reliability, average variance extracted values, and correlations between each factor
| Factor | No. of Items | CR | AVE | 1 | 2 | 3 | 4 |
|---|---|---|---|---|---|---|---|
| ELA | 15 | 0.899 | 0.377 | 0.614^ | |||
| IR | 11 | 0.874 | 0.392 | 0.803** | 0.626^ | ||
| AL | 9 | 0.834 | 0.362 | 0.854** | 0.820** | 0.602^ | |
| ML | 4 | 0.736 | 0.412 | 0.762** | 0.717** | 0.721** | 0.642^ |
^square root of AVE, **
Fig. 5Theoretical framework of online peer learning