Pre-Service Teachers’ Views of the Value of Cooperative Learning: How Can it Shape 21st-Century Skills?
Bibliographic record
Abstract
Teacher education should provide pre-service teachers with the opportunity to use pedagogical approaches aligning with 21st-century skills, such as cooperative learning. This present, mixed-methods study aims to examine how cooperative learning may promote (or not) the development of the abilities that are needed for this era (i.e., collaboration, creativity, interest, engagement, and self-regulation), according to the participant pre-service teachers who attended a general-didactics course, completed a survey, and carried out a SWOT analysis. The quantitative results show that each of the 21st-century skills was developed, but to a different extent. The qualitative data, along with its integration with the quantitative data, provided a more comprehensive understanding of how and why each of the aforementioned proficiencies was developed, either confirming, or further explaining, the quantitative results. In conclusion, cooperative learning may foster the development of 21st-century skills, which are essential in the pursuit of quality education. Although no claims are made regarding the generalization of the findings, this study supports the idea that integrating cooperative learning in teacher education may promote relevant abilities and competencies among pre-service teachers, while providing them with experiences that may shape their future teaching practices.
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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 | 0.005 | 0.011 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".