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Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine
Indy Man Kit Ho1, Kai Yuen Cheong1, Anthony Weldon1
1Technological and Higher Education Institute of Hong Kong (THEi), Chai Wan, Hong Kong.
Plos One
|April 2, 2021
Summary
Student satisfaction with emergency remote learning (ERL) during COVID-19 was neutral. Face-to-face learning preference was the strongest predictor, alongside instructor effort and assessment appropriateness.
Area of Science:
- Higher Education
- Educational Technology
- Pandemic Pedagogy
Background:
- The COVID-19 pandemic necessitated widespread adoption of emergency remote learning (ERL) in higher education.
- Understanding student satisfaction factors in crisis-driven ERL is crucial but limited.
Purpose of the Study:
- To identify key predictors of undergraduate student satisfaction with ERL.
- To compare the predictive accuracy of multiple regression and machine learning models for ERL satisfaction.
Main Methods:
- Investigated 425 undergraduate students at a Hong Kong university using Moodle and Microsoft Teams for ERL.
- Employed and compared multiple regression and machine learning models, including random forest recursive feature elimination and elastic net regression.
Main Results:
- Elastic net regression achieved the highest accuracy (65.2% explained variance).
- Overall ERL satisfaction was neutral (4.11/7).
- Preference for face-to-face learning was the most significant predictor, followed by instructor effort, assessment appropriateness, and perceived quality of online delivery.
Conclusions:
- Despite technological competence, students prefer traditional learning over ERL.
- Enhancing ERL quality requires reviewing assessment strategies and optimizing structured, interactive delivery tailored to program needs.
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