Learning in Higher Education Under the Covid-19 Pandemic: Were Students More Engaged or Less?
Bibliographic record
Abstract
This study explored students’ learning experiences in higher education during the Covid-19 pandemic. A journal writing methodology was used to extract learners’ reflective thoughts regarding their living and learning during the pandemic outbreak. The results were interpreted through the views of relevant student engagement frameworks. The students’ structural factors (family, support, and pressure) were impacted because of political and sociocultural factors (restrictive measures in response to the pandemic outbreak) within which the university factors were embedded (total closure with online education, subsequent reopening allowing physical attendance, and later principal distance education with approved exceptions), which collectively and psychosocially influenced students’ life and studies. The learners self-adapted via their individual efficacy to tackle the unfamiliar situations by digitally reaching out to family/friends and enhancing skills/self-learning; learner differences in learning style and preferences were noted. Online courses offered flexibility for learning independent of time and space while social presence in the learning community during online lessons remained less effective; traditional values of face-to-face physical classrooms were recognised among some learners. Learners’ perceived effective engaging measures underscored the importance of ensuring learner well-being (counselling and mask-wearing), learning independence (online lecture recordings and optional attendance), and strengthening online learning experiences (building the learning community, enhancing class dialogue, and demonstrating problem-solving techniques). Recommendations for engaging learning were discussed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".