Teachers’ Strategies of Including Learners with Autism Spectrum Disorders in Mainstream Classrooms in Swaziland
Bibliographic record
Abstract
Successful inclusion of students with autism spectrum disorder (ASD) in general education classrooms can be challenging and may require additional supports for teachers.The purpose of this study was to explore the strategies that teachers use in including learners with autism spectrum disorders in the mainstream classrooms.This qualitative study draws on a purposive sample of 36 teachers who have experience teaching children with ASD within three primary schools that practice inclusive education and have rich cases of learners with ASD in Eswatini.Through the use of focus group discussions, individual interviews and observations data was collected on teachers strategies they employ in their teaching.Conventional content analysis were used to analyse data and thematically presented.Teachers reported several strategies including: pictography, learner fixations, routine, motivation, and sitting arrangement.Conclusions are made that teachers in the mainstream classrooms have knowledge on some of the strategies for including learners with ASD.However, they are not well capacitated to implement these strategies.Teachers recommended frequent workshops for teachers on strategies to employ in teaching children with ASD in the mainstream classrooms.Coming with assessment tools that are realistic to our society in terms of development and inclusive education programmes would yield positive results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".