Experiences and Impact: The Voices of Teachers on Math Education Reform in Ontario, Canada
Bibliographic record
Abstract
In Ontario, students’ declining math performance is currently cited as a major area of concern (Reid & Reid, 2017). In response to this, Ontario is implementing math education policy changes. However, there is no mention of the role of teachers in this reform process. To address this issue, this paper explores and shares teachers’ experiences with math reform. I took a qualitative approach and interviewed eight public school teachers who shared their experiences with math reform based on their teaching trajectories. Three themes emerged from the data: (1) math confidence impacts perception and response to math reform; (2) teachers have little to no active role in the math reform process; (3) there is bidirectional impact between math reform and teachers. These findings delineate significant implications for math reform; the need to revere firsthand accounts of teacher experiences and insights, treating teachers as change agents, and engaging teachers in math reform processes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.042 | 0.021 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".