Mathematics education reform:The role of coherence within the complexity of change
Bibliographic record
Abstract
This paper draws on data gathered from a large-scale, multi-year research project, Curriculum Implementation in Intermediate Mathematics (CIIM), that examines the implementation of a reform (inquiry-oriented) mathematics curriculum in Grades 7 – 10 in Ontario, Canada. To describe classroom practices and ways that teachers have been challenged and supported in implementing an inquiry-oriented approach, the data included teacher questionnaires (n =1096), focus group interviews with mathematics educators across the province, and nine case studies. While some of our data align with the research of others who show that teacher change is complex and inquiry-oriented pedagogies are slow to emerge (Frykholm, 1999; Jacobs, Hiebert, Givven, Hollingsworth, Garnier, & Wearne, 2006), we also have evidence of teachers engaged in a variety of classroom practices that involve students in inquiry-oriented mathematics learning.
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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 | 0.020 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.031 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| 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".