Le soutien à l'autodétermination: un appui à la participation des élèves présentant des difficultés comportementales dans l'établissement de leur plan d'intervention.
Bibliographic record
Abstract
When a student is unable to progress and succeed within his training program or the situation requires the implementation of specialized services or various adaptations, an individualized education plan (IEP) is implemented (Ministère de l'Éducation du Québec [MÉQ], 2004a). Although the Quebec framework for the establishment of the IEP specifies that the student must be placed at the heart of his or her success, the participation of students with emotional and behavioural difficulties (EBD) is generally little solicited, their opinions little taken into account, and they do not feel prepared to play an active role in it (Souchon, 2018). However, many studies demonstrate the positive effects associated with the active involvement of the student in the establishment of his IEP. Based on an integrative review of literature, this article paints a portrait of the situation regarding the establishment of IEPs and the participation of EBD students in this process. It outlines the benefits associated with active participation on their part. Subsequently, the need to support students in order to support this participation is demonstrated. Finally, courses of action are proposed to support EBD students in the development and implementation of their IEP and to support their self-determination throughout the phases of this process.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".