The Role of Environmental Education in the Ontario Elementary Math Curriculum
Bibliographic record
Abstract
In 2007, the Ontario Ministry of Education (OME) mandated environmental education (EE) be integrated into all subject areas. However, in the Ontario math curriculum, there is not a single specific expectation that makes reference to EE. An initial literature review revealed there is a significant research gap when connecting math and the environment at the elementary level. Very few techniques, strategies and specific activities are proposed to help teachers integrate EE within the math curriculum. The primary research question that guides the study is: What role does environmental education play in the Ontario math curriculum at the elementary level? A qualitative case study approach is used as data is collected through individual, semi-structured interviews. Codes are created to identify two overarching themes. There are many challenges that restrict teachers from easily integrating EE and math. However, opportunities do exist for schools to overcome these challenges and successfully integrate EE and math. The OME must take a more active role in providing practical solutions that link EE and math. Teachers must be reflective and motivated to make their own connections between EE and math. Finally, administrators must encourage widespread integration of EE within all subjects, including math.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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, 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".