From Curriculum Design to Program Implementation: Filling in the Gaps
Bibliographic record
Abstract
The purpose of this paper has been to reflect on the design and implementation of the four-term ETEP teacher education program that was introduced in Faculties of Education across Ontario in 2017 to reflect the legislation that mandated a longer teacher preparation experience through Ontario Regulation 347/02 (as revised Dec. 1, 2014 - Aug. 31, 2015). Predictably each program across the province proceeded with different program structures within the mandated framework, addressed special features of their program differently, and incorporated different features into the resulting program. In this paper, we explore how rushed implementation resulted in gaps in design and implementation of a program; we examined these gaps and circumstances that led to them in the context of historical labour disruption, and structural changes in the management of the university. These gaps are attributed to a variety of factors. The major contribution of this paper includes a series of models for curriculum design and implementation specific to the design of the ETEP, but useful for curriculum design and implementation in any context. We propose that opportunities to re-engage in the program design process in a fulsome, visionary way to take advantage of the input we have had from faculty, teacher candidates, and associate teachers over the first years of the new approach to teacher certification in the province should be considered.
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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 | 0.110 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.013 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".