From Crisis to Continuity: Exploring Students’ Perspectives on the Future of Online Learning Beyond COVID-19
Bibliographic record
Abstract
As we live in the post-COVID-19 era, much research should be devoted to guiding educators and policymakers on what to retain, revise, or even eliminate from the online learning experience. This study aimed to provide a deeper understanding of the students’ behavioural intention to continue using technology in the post-COVID-19 era. The study was grounded in a well-known theoretical model for assessing technology adoption, the Technology Acceptance Model (TAM), expanded by adding the following external variables: accessibility (ACC), anxiety (ANX), feedback (FB), computer playfulness (CP) and perceived enjoyment (PNJ). A total of 134 undergraduate students from both public and private universities and colleges in Oman were included in the study. Data was collected through the administration of a Likert-scale questionnaire and analysed using descriptive tests and the Smart-PLS technique. The study’s main findings revealed that ACC, ANX, CP, and PNJ had a significant impact on Perceived Ease of Use (PEOU), while no such effect was observed on Perceived Usefulness (PU). Notably, the study concludes that students exhibit a high intention to continue using technology. The study underscores the increasing familiarity of interactive technology tools among teachers and students, a trend accelerated during the pandemic. However, a recommendation is made for the development of a comprehensive framework by educational stakeholders, including policy professionals and teachers, to specify the strategic use of technology and its intended purpose.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".