Playing through the pandemic and beyond: exploring the ongoing impact of COVID-19 on play-based learning in kindergarten classrooms
Bibliographic record
Abstract
The response to the COVID-19 pandemic impacted educational systems throughout the world. School closures, virtual schooling, and strict safety protocols for in-person learning changed instructional approaches to teaching. These changes were particularly disruptive in early childhood education which relies on play-based learning to support students’ socioemotional and academic development. This study examines the impact of school disruptions on play-based learning during the pandemic and the lasting effect on classroom practices from the perspective of kindergarten educators in Ontario, Canada. An online survey was administered to 100 kindergarten educators with open-ended questions regarding how the pandemic shaped their implementation of play, challenges they faced during this time, and how their practices have changed in the years following the pandemic. Results indicated significant impacts on play-based learning due to changes to the physical space and materials available, decreases in student choice, limits to social interaction, and masking protocols. Post-pandemic, educators indicated some lasting effects on the implementation of play in their classrooms and noted concerns regarding students’ social-emotional development and gaps in their academic skills. These findings provide insight into the continued influence of the pandemic on education and how educators are responding to these lasting impacts.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".