COVID-19 Pandemic as A Catalyst for Fostering Reformed Pedagogy in Science Education
Bibliographic record
Abstract
The study examined the role of COVID-19 pandemic as a catalyst for fostering reformed pedagogy in science education within the South African context. The prevalence of COVID-19 pandemic compelled teachers as key agents of educational change to fundamentally rethink their pedagogical practices with a view to bring about reformed pedagogy. The study adopted a phenomenological design located within the critical paradigm. The empirical investigation involved 21 purposively selected in-service science teachers enrolled for postgraduate studies in science education at a South African university. Critical theory was adopted as a theoretical lens to provide insightful elucidation into how science teachers negotiated and transformed their pedagogical practices in response to the formidable challenges posed by COVID-19 pandemic. The COVID-19 pandemic critically exposed socio-economic disparities in science teaching and learning within the broader South African context. Under-resourced schools represented inappropriate educational entities which rendered encouragement of critical thinking and promotion of innovative pedagogical practices extremely difficult to realize. Science teachers at under-resourced schools were largely left to their own devices when navigating formidable challenges posed by the prevalence of COVID-19 pandemic. This dilemma represents a structural problem that ought to be addressed as a matter of priority in order to ensure social justice in terms of the creation of conducive teaching and learning environments at under-resourced schools in particular. Meaningful transformation of pedagogy remains an arduous task in the face of fundamental challenges afflicting teacher professional growth and its ramifications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".