Why is it difficult for schools to establish equitable practices in allocating students to attainment ‘sets’?
Bibliographic record
Abstract
Research has consistently shown ‘ability’ grouping (tracking) to be prey to poor practice, and to perpetuate inequity. A feature of these problems is inequitable and inaccurate practice in allocation to groups or ‘tracks’. Yet little research has examined whether such practices might be improved. Here, we examine survey and interview findings from a large-scale intervention study of grouping practices in 126 English secondary schools. We find that when schools are encouraged to allocate students and move them between groups according to equitable principles by participation in a ‘best practice’ intervention, there is some increased equity of practice (i.e. a reduction in non-attainment factors used in allocation). However, the majority of schools continue to use subjective and potentially biased information to group students. Furthermore, some schools that claim to be using attainment setting appear to be using the inequitable practice of streaming. Our findings show that improvements in equity are constrained by operational and strategic factors, including timetabling, finance, and teachers’ values and beliefs relating to student ability and progression. We suggest strategies for encouraging schools to change their grouping practices, drawing on approaches for working with complex organisations.
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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 | 0.055 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.003 |
| 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".