Learners’ attitudes to mixed-attainment grouping: examining the views of students of high, middle and low attainment
Bibliographic record
Abstract
There is a substantial international literature around the impact of different types of grouping by attainment on the academic and personal outcomes of students. This literature, however, is sparse in student voices, especially in relation to mixed-attainment practices. Research has indicated that students of different attainment levels might have different experiences and views of grouping structures. This paper represents a significant contribution to this literature. Drawing on the data collected as part of a large study on student grouping and teaching in England, we analyse the attitudes of students of different attainment levels to mixed-attainment practice, focusing on their explanations for their preferences or aversion to mixed-attainment classes. The data-set is drawn from group discussions and individual interviews with 89 students age 11/12 (Year 7) from eight secondary schools practicing mixed-attainment grouping in mathematics and English. Our analysis identifies some broad patterns in student attitudes, including a strong preference for mixed attainment among those at lower prior attainment. The analysis of the explanations students give for their opinions on mixed-attainment practice demonstrates how the learner identities of different groups of students are constituted in various ways by the discourses around ‘ability’, and constrained by the dominant ideology of ‘ability’ hierarchy.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".