Factors for Sustainable Online Learning in Higher Education during the COVID-19 Pandemic
Bibliographic record
Abstract
The coronavirus disease 2019 (COVID-19) pandemic has affected educational institutions and instructors in an unprecedented way. The majority of educational establishments were forced to take their courses online within a very short period of time, and both instructors and students had to learn to navigate the digital array of courses without much training. Our study examined factors that affect students’ attitude toward online teaching and learning during the COVID-19 pandemic. It is different from other online learning studies where online courses are mostly a method of choice, with suitable support from institutions and expectation from instructors and students, rather than a contingency. Under this specific environment, we utilized an online survey to collect students’ feedback from eleven universities across Hong Kong. Using partial least squares for analysis on the 400 valid samples we received, we found that peer interactions and course design have the most salient impact on students’ attitude, whereas interactions with instructors has no effect at all on students’ attitude. Furthermore, we also provide suggestions on using the existing technologies purchased during COVID-19 for a more sustainable learning environment going forward.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".