Online learning dexterity

The adoption of online learning in the education sector was accelerated by the unprecedented COVID-19 pandemic. Online learning dexterity is unpacked into six dimensions: asynchronous learning dexterity, synchronous learning dexterity, self-directed learning dexterity, online collaboration dexterity, learning technologies dexterity, and learning access dexterity. This study formulated a predictive model of online learning dexterity on students’ academic performance. The study adopted a quantitative method using the deductive approach to establish the impact of the six dimensions of online learning dexterity on academic performance in Singapore. Participants (N = 150) were randomly sampled through a non-probability sampling approach. Using Smart-PLS version 4 to assess measurement and structural models, PLS-SEM techniques were employed. The model achieved constructs’ reliability and validity for both measurement and structural model.

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Institutions

Publication Details

Journal
American Journal of STEM Education
Published
2025-04-07
DOI
https://doi.org/10.32674/s8nqcf98
Citations
1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
1.69
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article

Online learning dexterity

Guowu Tan
1 citations
American Journal of STEM Education
Teaching and Learning Programming
1.69
article

Online learning dexterity

Guowu Tan
article en
1 citations

Abstract

The adoption of online learning in the education sector was accelerated by the unprecedented COVID-19 pandemic. Online learning dexterity is unpacked into six dimensions: asynchronous learning dexterity, synchronous learning dexterity, self-directed learning dexterity, online collaboration dexterity, learning technologies dexterity, and learning access dexterity. This study formulated a predictive model of online learning dexterity on students’ academic performance. The study adopted a quantitative method using the deductive approach to establish the impact of the six dimensions of online learning dexterity on academic performance in Singapore. Participants (N = 150) were randomly sampled through a non-probability sampling approach. Using Smart-PLS version 4 to assess measurement and structural models, PLS-SEM techniques were employed. The model achieved constructs’ reliability and validity for both measurement and structural model.

American Journal of STEM EducationVol. 9
Nanyang Polytechnic (SG)
Openalex Percentile: Top 14%
Teaching and Learning Programming
1.69
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