The New Normal in University Education after the Coronavirus Pandemic
Bibliographic record
Abstract
The recent Coronavirus pandemic had serious impact on many aspects of our lives, and higher-education is not an exemption. In February 2020, more than half-way through the winter 2020 semester, university campuses closed and suddenly, all courses had to be offered virtually. With or without experience with online learning, professors had to quickly adapt to this new teaching environment. To everybody’ surprise, the situation lasted until the summer months of 2022. In September 2022, universities re-opened and in-class teaching resumed. However, there were signs that teaching and learning could not go back to the pre-pandemic settings. In this paper, using a survey instrument administered at the end of nine semesters in a graduate course in a Canadian business school; from pre-pandemic to post pandemic semesters, changes in attitudes towards Managerial Analytics, changes in perceptions towards learning, and changes in the levels of anxiety when facing different situations are identified. The new normal is assessed and found to differ from the pre-pandemic period in many aspects.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".