Co‐operative learning for students with difficulties in learning: a description of models and guidelines for implementation
Bibliographic record
Abstract
As part of a larger study regarding the inclusion of children with disabilities in mainstream classroom settings, Ellen Murphy, of the D Clin Psych programme at NUI Galway, with Ian Grey and Rita Honan, from Trinity College, Dublin, reviewed existing literature on co‐operative learning in the classroom. In this article, they identify four models of co‐operative learning and specify the various components characteristic of each model. They review recent studies on co‐operative learning with the aim of determining effectiveness. These studies generally indicate that co‐operative learning appears to be more effective when assessed on measures of social engagement rather than academic performance. Finally, Ellen Murphy, Ian Grey and Rita Honan present their account of the factors that contribute to the successful implementation of co‐operative learning for students with difficulties in learning.
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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 | 0.034 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".