Pre-service and in-service teachers’ attitudes and self-efficacy beliefs with regards to inclusive education
Bibliographic record
Abstract
Pre-service teachers (n = 1572) from five faculties of education in Ontario, Canada were surveyed at the beginning of their programme about their attitudes and self-efficacy beliefs regarding inclusive practices. These responses were compared to responses from Ontario in-service teachers (n = 739) prior to their district-wide shift from a model of segregation to inclusive classrooms. Results demonstrate that elementary and female pre-service teachers held the most clearly defined inclusive growth mindset and indicated a greater level of confidence in communicating with and supporting families of students with disabilities as compared to secondary and male pre-service teachers. Male pre-service teachers had higher self-efficacy than females for managing behaviour in the classroom. Pre-service teachers, as compared to in-service teachers, favoured a student-centered classroom that promotes student choice and differentiated instruction and they indicated greater confidence in their ability to engage students with accommodations. Pre-service teachers held distinct attitudes toward the role of the students in their own learning and their responsibility to teach to all. It is important to note that both sets of participants were at the beginning of their inclusive practice, therefore, the role of lived professional (or lack thereof) experience may be impacting their attitudes and self-efficacy beliefs about inclusion.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".