Predicting the Roles of Attitudes and Self-Efficacy in Readiness Towards Implementation of Inclusive Education Among Primary School Teachers
Bibliographic record
Abstract
Teachers' preparedness is a critical component in implementing inclusive education. It is pertinent to understand whether mainstream instructors are ready for inclusion as the number of children with special needs increases steadily over the years. The Zero Reject Policy has accelerated the implementation of inclusive education in Malaysia. While this is an essential step forward, assessing teachers' readiness for change is critical. This study aims to find out the predictive factors (attitudes and self-efficacy) on the preparedness of mainstream primary school teachers towards the implementation of inclusive education. This study is of a correlational research design where questionnaires were distributed to 367 teachers randomly selected from a cluster of nine schools in Hulu Selangor, Malaysia. The results show that teachers have moderate levels of readiness, attitudes and self-efficacy. There are also significantly positive relationships and predictive correlations between attitudes and readiness as well as self-efficacy and readiness. This implies that attitudes and self-efficacy should be considered in gauging teachers' readiness in the implementation of inclusive education. Taken together, findings in this study could inform further inclusive education research in Malaysia and could be taken into consideration in the design and execution of teacher training courses on 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".