Students’ Intention to Take E-Learning Courses During the COVID-19 Pandemic: A Protection Motivation Theory Perspective
Bibliographic record
Abstract
This study proposes a new model for integrating the protection motivation theory (PMT) with the technology acceptance model (TAM) to explore factors affecting students’ intention to attend e-learning courses during the COVID-19 pandemic. A total of 432 valid responses to an online questionnaire were received from freshmen students studying in universities in Vietnam and Taiwan. Structural equation modeling was used to evaluate the proposed research model and test the hypotheses, and model evaluation reflected a good fit between the data and the proposed research model. Differences between perceived vulnerability, perceived severity, and intention to take e-learning courses across two countries were also established, suggesting that both the TAM and the PMT should be considered for use in studies related to technology adoption in the pandemic context. The factors influencing students’ intentions to take online courses can be quite varied when different educational settings are considered; therefore, a more contextual understanding of students’ e-learning intentions during pandemic times should be carefully examined. Suggestions for governments and policy makers are also proposed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".