Assessing teachers’ interpretation of Canadian large scale assessment results: An innovative approach to piloting a questionnaire
Bibliographic record
Abstract
Current media debates surrounding the usefulness and the validity of provincial large scale assessments have called into question their purposes, the many uses and their unintended consequences in teaching practices in the Canadian context. While many argue that large scale assessments may be harmful as they trigger anxiety for both teachers and students, these results can also provide teachers with accurate and timely feedback on student learning. This is why it is important to consider the social and cognitive factors that may influence teachers' interpretation of large scale assessment results. In so doing, we aim to describe how these results act as an external stimulus that creates a positive or a negative cognitive dissonance for teachers and the factors that may influence their interpretation of these results as a source of feedback. In this presentation, we will present the first steps of the adaptation of a questionnaire that allow to measure these factors. Examining the extent to which psychological factors, such as perception of high social influence from various stakeholders, influence teachers in using these results may provide a better understanding of the observed behaviors in teaching practices.
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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 | 0.054 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".