A Cross-National Study of the Effects of Education on Pre-Service Teachers’ Attitudes, Intentions, Concerns, and Self-Efficacy Regarding Inclusive Education
Bibliographic record
Abstract
This study reports on a cross-national comparison of inclusion-oriented teacher-education programs. Canada and Germany have implemented inclusion in teacher education with the aim of improving inclusion in schools. Previous studies have shown the importance of latent characteristics of prospective teachers for the successful implementation of inclusion in schools and have pointed to the role of inclusion-oriented teacher education in developing these teacher characteristics. To measure potential changes in attitudes, intentions, concerns, and self‑efficacy, 132 student teachers from Germany and Canada were surveyed before and after a course about inclusive education. Internationally validated scales were used: Attitudes towards Inclusion Scale (AIS), Intention to Teach in Inclusive Classroom Scale (ITICS), Concerns about Inclusive Education Scale (CIES), and the Teacher Efficacy for Inclusive Practices scale (TEIP). The results of the German and Canadian groups differed: while significant changes in self-efficacy occurred between the first and second measurement points in both countries, the outcome for other factors varied. Significant changes in intentions to use inclusive teaching practices were found in Canada but not in Germany. The results are discussed in the context of the role of teacher-education programs in fostering inclusive teaching practices.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".