Integrating Practice and Theory in Teacher Education: Enhancing Pre-Service Self-Efficacy for Inclusive Education
Bibliographic record
Abstract
Inclusive education demands that children worldwide have access to education alongside their peers in their neighborhood schools and within regular classrooms. Understanding experiences that contribute to pre-service teachers’ self-efficacy is important as it influences their readiness to enact inclusive strategies effectively. This study involved 69 pre-service teachers from Canadian faculties of education in mixed-methods research using Group Concept Mapping. The analysis identified five clusters of experiences; a repeated measures ANOVA revealed that ‘Applying Knowledge’ and ‘Collaborating with Colleagues’ comprised the most important experiences for contributing to pre-service teachers’ self-efficacy for inclusive practices, while ‘Community Support’ and ‘Experiences with Diverse Student Needs’ were significantly more important than ‘Professional Development’. By aligning these clusters with Bandura’s sources of self-efficacy, this study highlights the importance of mastery experiences and supportive interactions. These findings suggest that teacher education programs that focus on and enhance practical experiences and interpersonal supports will connect to and better prepare pre-service teachers for inclusive education.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".