Re-imagining Pedagogy for Early Childhood Education Pre-service Curriculum in the Face of the COVID 19 Pandemic
Bibliographic record
Abstract
COVID-19 has caused a change in the demography of the Early Childhood Education (ECE) teaching fraternity. This paper problematizes the ECE curriculum delivery gap created by the influence of COVID-19. Central to this paper is the assumption that online learning might limit the stimulation of children’s holistic development. Reflecting on their experiences, the authors examine the influence of COVID-19 on pedagogy for ECE pre-service and kindergarten curriculum through the lens of design thinking theory. The reflections are based on one open and distance learning institution in South Africa and one kindergarten program in Canada. The findings reveal challenges, such as digital inequality brought on by socio-economic imbalances and opportunities. The authors suggest the digital literacy skills needed to mitigate the influence of COVID-19 in the ECE pre-service and kindergarten curriculum delivery.
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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 | 0.010 | 0.010 |
| 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 | 0.017 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".