Analyse des outils d'accompagnement et d'évaluation : point de vue des formateurs de terrain
Bibliographic record
Abstract
Over the past three years, the REEVES Collaborative Research Group has studied the evaluation practices of cooperating teachers (CTs) through various field projects. The present article, based on one of these projects, seeks to gain input from CTs and to analyze the efficiency, effectiveness and pertinence of assessment tools and descriptive scales with regard to how CTs make decisions about the proficiency levels of student teachers. Drawing on evaluation grids from practicum handbooks developed by universities in the province of Quebec, the team asked CTs to identify which ones are the most helpful, pertinent and useful for decision making when evaluating (Figari & Remaud, 2014; Tricot & Tricot, 2000). These grids are typically developed by program heads, sometimes in cooperation with university supervisors and, more rarely, with cooperating teachers (Belair, Vivegnis & Lafrance, 2015; Lapointe & Guillemette, 2015; Portelance, 2010). They also provide a reference framework because they reflect program focus, which often differs considerably from one program to another—even within the same university. However, CTs must constantly adapt to different program objectives and evaluation tools, causing unease and confusion over how to fill out grids and make evaluation decisions. Preliminary results suggest that none of the proposed grids perfectly meet their needs. Verbatim analysis from focus group helped identify useful, coherent and facilitating elements for making informed and fair decisions about student teachers. Possible links between the choice of grids and scales, and the potential impact on evaluating proficiency level is also discussed and avenues for further research are proposed.
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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 | 0.051 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".