1. Constructing a Student Engagement and Learning Development Model in Mobile Learning by SEM
- Author
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Shu-Jing Wu, Feng-Lan Liu, Yan-Yu Xu, Tin-Chang Chang, and Zeng-Han Lee
- Abstract
This study aimed to build a model to detect the factors to enhance student engagement and learning development in mobile learning during the COVID-19 Pandemic. Data from a total of 400 junior-high-school students were collected in China in the fall semester of 2020, and a large proportion of students preferred accessing their study with cellphones (67.0%) than with computers (11.8%), laptops (11.3%) or pads (10.0%). Exploratory factor analysis and structural equation modeling (SEM) were used for data analysis. The fitness of the items for each scale of the student engagement and learning development indicated a sufficient fit (X[superscript 2][subscript (19)] = 41.252, GIF = 0.974, AGFI = 0.951, CFI = 0.986, RMSEA = 0.054, NNFI = 0.979, IFI = 0.986). The results of SEM analysis show that emotional engagement is the most important factor (r[superscript 2] = 0.859) in the model, and student engagement has a significant positive impact on learning development in mobile learning. The findings of this study provide a good reference for enhancing student engagement or fostering students' learning development in mobile learning. [For the full proceedings, see ED639391.]
- Published
- 2023