Teachers’ experiences with dyslexic learners in mainstream classrooms: Implications for teacher education
Bibliographic record
Abstract
Inclusive education represents the main ethos of the Kingdom of Eswatini education system. This reflects on both the Constitution and on various education policies since the country became a signatory to the goals of Education for All. However, it would appear that major constraints impede the education vision that resonates through the charter of ‘no child is left behind’. The education of learners with dyslexia is then called into empirical questions with a focus on the experiences of teachers of such category of learners within the Eswatini education system. A phenomenological research design was chosen, using a convenience sampling technique to select 12 English language teachers of dyslexic learners. Data were obtained by individual semi-structured interviews and by non-participant observations. Content analysis was employed to analyze the data, which were then presented thematically. Peer review, as well as member checks, were used to improve the trustworthiness of data. The main themes that emerged were insufficient time, unwelcoming attitudes, lack of support, and lack of training of teachers of dyslexic learners. It was equally evident that teachers were challenged by insufficient training to enable them to deal with dyslexic learners. Without an effective support structure for teachers, the education of dyslexic learners would remain a chimera. This finding implicates the teacher education programs in colleges of education and universities in the sense that training on inclusive classroom teaching should form part of the teacher education program.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".