Educational provision for less able students of English and Maths
Bibliographic record
Abstract
Current plans to reform General Certificate of Secondary Education (GCSEs) in England and Wales include a return to linear assessment, the inclusion of more challenging course content, and an increase in demand at the level of what is widely considered to be a pass (Department for Education, 2013). Although these changes may help to stretch the most academically able 14 to 16 year olds, facilitating their progression to A levels and beyond, it is also important to ensure that secondary education caters for the full ability range. Students who struggle with core academic subjects also have a valuable contribution to make to society and the economy. Their educational achievements should be as significant a national concern as those of their more able peers. In this article, we compare provision for equivalent students in four of the highest performing jurisdictions around the world: Singapore, New Zealand, Alberta (Canada) and Hong Kong. We also explore existing educational provision for less able 14 to 16 year old students of English and Mathematics in England. Although cultural and societal differences provide good reasons to discourage direct policy-borrowing, international comparisons may nevertheless reveal some useful approaches for consideration.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".