Promoting Inclusive Education Practices Ii Elementary School Through Formative Assessment
Bibliographic record
Abstract
Following the 2003 curricular reform, assessment in Quebec has been governed by three overarching values: justice, equality, and equity (MEQ, 2003). Those values find their origin in inclusive education, which promotes a competency-based assessment and the success of all students (Akkari and Barry, 2018). However, with the return to numerical results, research has shown that teachers still favoured a summative assessment approach thus failing to uphold the core values of the programme (Hadji, 2015; Issaieva and Crahay, 2010). Consequently, teachers' evaluation practices still classify students as "good" or "bad" depending on their results. How can this situation be modified? In order to find answers to this problem, collaborative research (Desgagné, 2001) with seven elementary school teachers was realized from 2016 to 2018. The results of the focus-groups identified avenues to develop a more inclusive form of assessment. This article offers a reflection on the importance of teachers’ understanding of disciplinary epistemology as the key to gaining greater freedom in assessment practices and thus promoting a more inclusive form of assessment.
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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 | 0.032 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".