The Perceived Efficacy of Cooperative Group Learning in a Graduate Program
Bibliographic record
Abstract
This paper addresses a gap in the literature about the study of the implementation of cooperative/collaborative group learning, and the assessment of its efficacy in facilitating transformative learning in the context of graduate studies. These topics have been widely discussed in the scholarly literature at the K-12 and post-secondary (college and undergraduate) level for many years, and cooperative group learning has generally been found to facilitate student learning. What has not been addressed is the use of this form of group learning in graduate studies. This paper reports on an intentional model of cooperative group-learning used in a Master of Education in Higher Education program at the Ontario Institute for Studies in Higher Education (OISE) at the University of Toronto. A brief review of the literature that grounds this praxis, the elements of the model used, and a post-hoc analysis of the perceptions of 77 graduate students (90% response rate) surveyed in a case study regarding its efficacy in facilitating their learning are presented. The findings suggest that this model of group-based learning has the potential to enhance the process for transformative learning at the graduate level of education.
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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 | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".