Worrying about what others think: A social-comparison concern intervention in small learning groups
Bibliographic record
Abstract
Small-group learning has become commonplace in education at all levels. While it has been shown to have many benefits, previous research has demonstrated that it may not always work to the advantage of every student. One potential problem is that less-prepared students may feel anxious about participating, for fear of looking “dumb” in front of their peers. This study examines the impact of an intervention to reduce that sort of anxiety—termed social-comparison concern—in small learning groups at the university level. Over the course of an academic quarter (10 weeks), 144 students in 33 small learning groups participated in the study. Approximately one-third received an intervention designed to reduce social-comparison concern by modifying theories of intelligence and attributions for failure. One-third received a study-skills intervention, and the remaining third received no extra resources. The findings suggest that the intervention was successful and that instructors may want to infuse small-group work with discussion of the malleable nature of intelligence and of the reasons for academic success and failure.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".