Lessons from zoom-university: Post-secondary student consequences and coping during the COVID-19 pandemic—A focus group study
Bibliographic record
Abstract
The COVID-19 pandemic dramatically altered the model of university education. However, the most salient challenges associated with online learning, how university students are coping with these challenges, and the impact these changes have had on students' communities of learning remain relatively unexplored. Changes to the learning environment have also disrupted existing communities of learning for both lower and upper-year students. Hence, the purpose of our study was to explore how: (1) academic and personal/interpersonal challenges as a result of COVID-19; (2) formal and informal strategies used to cope with these academic and non-academic challenges; (3) and services or resources provided by the institution, if any, affected students' communities of learning. Six focus groups of 5-6 students were conducted, with two focus groups specifically dedicated to upper and lower year students. Questions related to academic and interpersonal challenges, formal and informal coping strategies, and access to/use of university services/resources were posed. Common challenges included poor accommodation from professors and administrators; burnout from little separation school and personal life; lack of support for students transitioning out of university; and difficulties forming and maintaining social networks. These findings suggest the importance of fostering communities of learning informally and formally at universities beyond the pandemic context.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".