An Exploration of Subject Curriculum and Policy Implementation in Ontario Schools: What Factors Support and Impede Effective Implementation?
Bibliographic record
Abstract
Ontario teachers experience updates to curriculum and policies at least every five years. The assessment document, Growing Success (2010), is expected to be in effect as of 2010. However, literature has shown that the concepts of Growing Success (2010) are not fully understood by teachers, and utilization of assessment methods vary. In addition, there are inconsistencies in the implementation of policy and subject curriculum across subjects and schools. In this study, I attempted to answer the following questions: What actions have been taken to ensure that teachers are practicing the new policies and curriculum? What factors have influenced or impeded change in policy and curriculum implementation? And to what extent are teachers practicing new policies and curriculum in the classroom? These questions were explored through three semi-structured interviews with experienced Ontario teachers. The data revealed issues in the quality of formal professional development (PD), a lack of support from administrators, a gap between interpretations of documents among teachers and administrators, and a disregard for assessment practices by students, parents and guardians. These findings raise important issues that need to be addressed before successful implementation of new policies and curriculum can take place.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".