A Comparative Analysis of Quebec and Ontario: How each province approached elementary provincial numeracy curricula and corresponding Faculty of Education accredited Teacher Education programs during the period of 2003-2018
Bibliographic record
Abstract
The issue of declining student achievement in elementary mathematics can be correlated to the implementation of the curricula reforms that began in the 1990s. The NCTM standards guiding the reforms called for significant change in teachers’ instructional pedagogy and approach (NCTM 1989, 2000). The Ontario government was increasingly concerned about the downward trend on provincial and international mathematical assessments as “results over the 2003 to 2018 period show a steady decline in Canada’s math scores” (Allison & Geloso, 2021, p.5). Quebec is the only province in Canada that has not experienced decline on national and international elementary mathematics assessments; instead, it continues to maintain its status as one of the highest performing jurisdictions in the world (Brochu et al., 2013).This exploratory descriptive and mixed methods research study aimed to understand, from a comparative analysis of Ontario and Quebec, how each province approached the organization and structure of the elementary mathematics curriculum, and how Faculty of Education programs approached teacher training within this changed context. I undertook a quantitative and qualitative analysis of both provincial curricula utilizing the ‘Model of Numeracy in the Twenty-first Century’ (Goos et al., 2011) for coding through four dimensions: context, mathematical knowledge, tools, and dispositions. This process was followed by a case study that included one Faculty of Education from each province. For this study, I conducted interviews with six key informants (three faculty and or administrators from each university) to examine how faculties responded to the large-scale changes and described the theoretical findings of the curricula analysis. These interviews provided valuable insight into the vastly different training structures each Faculty of Education had for certifying teachers. The study findings highlight four key themes that emerged: (a) differences in Ontario and Quebec student achievement in the area of number sense and numeration; (b) differences in curricula structure (size and complexity); (c) statistical differences by province in the dimensions of context and mathematical knowledge; and (d) differences in the incorporation of problem solving, mathematical tools, theoretical underpinnings and the use of supplementary resources. The findings include implications for theory, policy and practice.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".