Key Drivers of Optimal Special Education Needs Provision: An English Study
Bibliographic record
Abstract
Abstract The aim of this paper is to argue that there are a number of key drivers for Special Educational Needs (SEN) provision that have to be met by Special Educational Needs Coordinators (SENCOs) and teaching professionals so as to ensure optimal provision and inclusion for children with SEN in mainstream primary schools. Although the research has been carried out in England, there is a significant European Dimension to the issue, as a similar role to that of SENCOs in respect of SEN management already exists in countries such as Finland and Ireland, and is being considered in Italy. This paper focuses on the data gathered for the purpose of the author’s doctoral research in England, through questionnaires and interviews with SENCOs, head teachers and teachers. Thematic analysis was used to explore key drivers of SEN provision by practitioners who support children with SEN. Data illustrate that the key drivers of SEN provision include time; teacher openness to change; target setting; evidence of tried interventions; empowerment; decision-making and approachability. The implementation of such drivers depend largely on practitioner skills and competencies. The main conclusion within this paper is to develop points of reference for planning and practice, with illustrations of optimal provision by all practitioners who work with children with SEN.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".