Environmental education during the COVID-19 pandemic: lessons from Ontario, Canada
Bibliographic record
Abstract
This study investigates the integration of place-based environmental education (PBEE) during emergency remote education (ERE) and in-person teaching, considering the implications of COVID-19. The objectives include (a) to understand if and how teachers used PBEE as a pedagogical learning tool during ERE, (b) to identify PBEE adaptations for implementation in an online context, and (c) to explore opportunities and barriers to PBEE during ERE. Ontario (Canada) educators’ perspectives were obtained through an online survey and focus groups. Using non-parametric statistical analyses, perspectives concerning opportunities and challenges to integrating and delivering PBEE in lessons were identified. Additional focus included educators’ views on student receptivity and knowledge retention. Findings indicate educators’ appreciation for PBEE as a pedagogical approach, yet delivery challenges arise from systemic barriers causing inconsistency in PBEE delivery. Obstacles include curriculum demands, institutional disinvestment, grade-level constraints, and limited training. Despite challenges, educators showcase innovation and commitment to PBEE during ERE, emphasizing its enduring value. The study underscores educators’ resourcefulness in adapting PBEE methods and the potential for renewed significance of outdoor education amidst the pandemic’s influence on students’ connection to nature.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".