The symbiotic roles of action research, lesson study and learning study seen in a social–emotional intervention for males with behavioural needs
Bibliographic record
Abstract
To help a group of nine adolescent boys with behavioural needs improve their social-emotional skills, the researcher designed and conducted a longitudinal intervention at a public secondary school in British Columbia. In order to complete this task, the researcher drew on the various strengths of action research, lesson study and learning study. Together their distinct attributes acted as an organizational framework and were seen to provide an effective and robust approach to school-based research. The action research component of the study ensured that the intervention had a deep understanding of the problems facing male students with behavioural needs, that stakeholders’ voices were considered in the research and that the personal role of the researcher was acknowledged. Learning study established positive masculinity as the intervention’s primary object of learning; and it helped influence the content and approach of individual sessions. Finally, lesson study’s fluid and collaborative approach to assessment allowed for ongoing guidance and a more comprehensive and nuanced understanding of the study’s results. Although there are similarities between action research, learning and lesson studies, the research discussed in this article demonstrates the efficacy and benefit in allowing their distinctive strengths to be used in conjunction for a longitudinal intervention.
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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 | 0.023 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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, 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".