Undergraduate Students' Perspectives on the Value of Peer-Led Discussions
Bibliographic record
Abstract
With a view to improving the quality of class discussions of assigned articles, I implemented a new way of organizing small group seminars in an undergraduate early childhood education course. The seminars were led by student facilitators and had a balance of accountability and autonomy. Mid-way through the course, the students reflected anonymously on the experience of the seminars. They identified a variety of cognitive and social benefits of the seminars as well as key components that could be applied in a variety of post-secondary settings. Dans le but d’améliorer la qualité des discussions de classe portant sur les articles que les étudiants devaient lire, j’ai mis sur pied une nouvelle manière d’organiser les séminaires de groupe dans un cours de premier cycle d’éducation de la petite enfance. Les séminaires étaient dirigés par des étudiants qui jouaient le rôle de facilitateurs et qui devaient concilier la responsabilité et l’autonomie. Vers le milieu du cours, les étudiants ont témoigné anonymement sur l’expérience des séminaires. Ils ont identifié une variété d’avantages cognitifs et sociaux que les séminaires leur avaient apportés ainsi que des composantes clés qui pourraient être appliquées dans toute une variété de milieux post-secondaires.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".