An International Comparison of Pre-service Teacher Attitudes towards Inclusive Education
Bibliographic record
Abstract
education of pre-service teachers in teacher training institutions in four different countries: Australia, Canada, Hong Kong, and Singapore. Using the Attitudes Towards Inclusive Education Scale (Wilczenski, 1995) and a series of demographic variables, this study concludes that pre-service teachers' attitudes towards inclusive education differ between countries across three factors which have been labeled academic and physical, social, and behavioral. Pre-service teachers in this sample are most positive about including students with social concerns. An examination of the items in this factor revealed that these issues, such as shyness, would rarely require immediate intervention and large amounts of extra time and effort on the part of the teacher in the same way that the behavior factor would, which rated low in terms of positive attitudes internationally. Demographic variables which can be emphasized by teacher training institutions such as close contact with a person with a disability, training, teaching experience, knowledge of policy and law, and confidence levels, all had a significant impact on attitudes. This paper argues that teacher training institutions should consider for inclusion in their programs practical experiences with inclusive education in positive and supportive environments, opportunities for students to experience success and reflection, and academic content regarding knowledge of policy and law relating to 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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".