Les effets d’un cours traitant de l’école inclusive sur les croyances de futurs enseignants au regard de leur sentiment de compétence en éducation inclusive
Bibliographic record
Abstract
The goal of this study, which was of an exploratory nature, was to evaluate the effects of an inclusive school course in which future teachers had the opportunity to systematically examine their own beliefs on the subject of inclusive education. For this purpose, two groups were formed: one experimental group (N = 25), which took the modified course, and one control group (N = 25), which took the same course but without modifications. At the beginning and at the end of the course, the two groups filled out a questionnaire on the subject of their beliefs about inclusive education. The statistical analysis carried out on the total score suggests that the experimental group did not obtain a significantly higher score than the control group did except in the domain of pedagogical competencies and leadership. Also, after having completed the modified course, the future teachers in the experimental group declared themselves to be more competent than those in the control group for the questionnaire items pertaining to seven out of eight indicators from the competence 4 norms of Alberta Education (MEA, 2018). Curiously, the future teachers from both groups felt incompetent with classroom management (indicator 6) whether they followed the modified course or not. These results seem promising, since they would indicate an interesting way of helping future teachers augment their feelings of competence in the area of inclusive education by means of a university course. Keywords: modified school inclusive course, future teachers, beliefs about inclusive education, feelings of competence
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".