Developing and Validating the Teacher Self-Efficacy for Teaching Students with Autism Spectrum Disorder (TSE-ASD) Scale
Bibliographic record
Abstract
Background: Autism spectrum disorder (ASD) continues to rise at an astonishing rate. As many schools attempt to create an inclusive environment conducive for students with autism to support academic success, we must recognize the teacher's role in creating an inclusive classroom. Using a student-specific teaching self-efficacy measure might provide more useful information for supporting teachers' beliefs for teaching students with ASD. Teachers with high self-efficacy have a positive impact on student achievement. The purpose of this investigation was to develop an instrument that can be used to measure teachers’ self-efficacy for effectively working with students with ASD. The original version of the scale was translated and back-translated into Persian, followed by a pilot study. A sample (n=633) of university students was recruited. Results indicated that the scale represented a unidimensional construct with acceptable internal consistency. Exploratory factor analysis demonstrated the unidimensionality of the TSE-ASD. The maximum likelihood confirmatory for the 12-item TSE-ASD model indicated excellent model fit indices (χ2/df=2.60, CFI=0.956, SRMR=0.049, PCLOSE >0.05, RMSEA=0.062, 90% CI [0.052, 0.082]). As for criterion-related validity, The Pearson correlation coefficients between (TSE-ASD score) and self-regulation (r= 0.72, p<0.01) revealed a large correlation and linear regression indicating that TSE-ASD significantly predicted self-regulation, b = 0.69, p < 0.001. Using a student-specific teaching self-efficacy measure might provide more useful information for supporting teachers' beliefs for teaching students with ASD. The findings provide evidence that TSE-ASD is a reliable and valid instrument for assessing teacher self-efficacy for teaching students with Autism Spectrum Disorder in educational settings among Persian speaking individuals.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".