Replication or Reinvention: Educators’ Narratives on Teaching in Higher Education During the COVID-19 Pandemic
Bibliographic record
Abstract
Objectives: The purpose of the study was to examine narratives about the effect of the sudden transition from face-to-face teaching to emergency remote teaching necessitated by the COVID-19 pandemic on post-secondary educators. Method: We conducted interviews with 11 post-secondary educators from five post-secondary institutes in one province in Canada. Educators were asked to reflect on their experiences during the transition from in-person to remote teaching and learning. Results: Our thematic analysis revealed that educators’ experiences were influenced by three main factors: (a) student engagement, interactions, and persistence in learning; (b) competence in the application of teacher technological pedagogical content knowledge (TPACK); and (c) overall well being of faculty and students. Conclusions: Participants had unique experiences, and institutions varied in the ways they supported students and staff. Those educators who had expertise, experience, or professional support in technology and teaching seemed to have an easier transition. Implication for Theory and Practice: Higher education institutes should support educators in enhancing their technological pedagogical knowledge and in facilitating learning in various delivery modalities.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".