Mathematics Curriculum Change: Identifying Parental Expectations
Bibliographic record
Abstract
Parents’growing concerns about the current approaches to learning mathematics in elementary school have drawn public attention in Canada. Rather than dismiss such concerns, understanding parent perceptions and garnering their support is essential to ongoing curriculum transformation and students’ success in mathematics learning. Using phenomenography, we examined parents’ perceptions of the current mathematics curriculum and their children’s experiences as expressed in community-based focus groups and individual interviews. Parents responded based on their past experiences, their views of children’s current experiences and their future aspirations for their children. Our analysis of parents’ perspectives revealed that their concerns and critiques were grounded in the expectations they held for their children’s mathematics learning. In particular, parental expectations fell into three categories: students need the opportunity to reach expected goals of mathematics learning; essential supports must be in place to reach expected goals; and, home-school communication is necessary for parent understanding and engagement. We suggest that by understanding the specific expectations that underlie parents’ concerns, teachers can engage in conversations that begin with affirming mutual expectations and respecting parents’ personal experiences to lead to partnering with parents as they realize their agency in their children’s 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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".