Evaluating the teaching and learning experience for the child with dyslexia in special and mainstream settings in Ireland
Bibliographic record
Abstract
This study examines and evaluates special provision for pupils with dyslexia in three different settings: reading schools, reading units and mainstream support. The research focused on the teaching and learning context for pupils with dyslexia, the support teacher, the mainstream teacher and the experience of the student. The main participants were teachers and tutors supporting pupils with dyslexia, and the parents of these children. Survey methods included questionnaires, focus group discussions, interviews and quantitative data on pupils' reading attainment. In addition, a total of six schools, two representing each model of support, were selected as case studies. This article reports part of a larger survey, which evaluated the effectiveness of three models of special provision for children with dyslexia in primary school. The study shows that there are academic and social benefits for the child with dyslexia who is enrolled in a special setting. However, placement in a reading school or reading unit per se does not guarantee that a child will ‘catch up’ with his or her peers. The findings reported a similarity in the methods and practices teachers use in both mainstream and special settings. The discussion suggests that if teachers are to ‘catch them before they fall’ there are serious questions that must be asked about how we are teaching basic literacy skills. The findings suggest an urgent need for a more balanced approach to teaching reading and writing.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".