The Immediate Impact of COVID-19 on Postsecondary Teaching and Learning
Bibliographic record
Abstract
Universities and colleges worldwide have quickly moved campus-based classes to virtual spaces due to the COVID-19 pandemic. This article explores the impact of this sudden transition of learning and teaching based on experiences and evidence from six institutions across three countries. Our findings suggest that although online and remote learning was a satisfactory experience for some students, various inequities were involved. Many students lacked appropriate devices for practical work and encountered difficulties in securing suitable housing and workspace. Students were stressed, and faculty were, too, especially those in precarious employment. The lack of fieldwork and access to laboratories created special challenges. We are concerned that the lack of hands-on experience could cause a decline in enrollments and the number of majors in geography over the next few years. This issue must be addressed by making introductory courses as engaging as possible. It is too early to determine the extent to which online and remote learning can replace campus-based, face-to-face geography education once the pandemic ends, but the new academic year of 2020–2021 will be revealing. Nevertheless, the COVID-19 crisis has revealed preexisting problems and inequalities that will need our collective effort to address, regardless of the pandemic’s trajectory.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".