Building Teacher Capacity within the Evolving Assessment Culture in Canadian Education
Bibliographic record
Abstract
Lost in the focus on large-scale educational assessments for accountability purposes is the important role of teachers' classroom assessment practices. Teachers must understand the use of both large-scale and classroom assessment practices and theories, and professional development remains the primary method to develop these assessment capacities. However, traditional models of professional development typically have little, if any, effect. In recognition of the importance of building teachers' assessment capacity, and the limitations of traditional professional development, the Elementary Teachers' Federation of Ontario, Canada, developed a Classroom Assessment Workshop Series to begin to build a systemic assessment framework for teachers. Through pre- and post-series surveys with 300 participants, and interviews and focus groups with facilitators, the authors' review and research explored the impact of the series on teachers' beliefs, self-efficacy, and knowledge of assessment practices and theory. The authors also explored the challenges that teachers experienced as they worked to understand and implement current conceptions of assessment. While teachers certainly valued the community created through the series and the opportunities to share their experiences, the findings found that teachers struggled to understand the theoretical foundations and use these foundations to further develop their own assessment practices. The research highlights the need for teachers to embrace a philosophy that integrates formative assessment practices and theories into their teaching and learning while also identifying the challenges associated with creating such an assessment culture. Current models of professional development may be more aligned with principles of effective professional learning, but truly changing teachers' classroom assessment practices may require a much more prolonged effort than those being provided.
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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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.029 | 0.015 |
| Scholarly communication | 0.017 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 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".