Exploring cooperative and challenge-based learning strategies as catalysts for personal resonance and transformation within and beyond the classroom: A case study
Bibliographic record
Abstract
Despite the widespread growth of cooperative and transformative learning strategies in higher education, there is limited research on how students collaboratively engage with critical incidents. We draw on challenge-based learning (CBL) principles to redesign a university course around collaborative and critical pedagogical principles to examine how they affect students’ self-perceived agency and responses to social problems. The results in this paper are based on qualitative summative feedback from students (n = 339) across three academic terms. We found that cooperative learning, guided by CBL, motivated students to respond to critical incidents in five transformative ways: deeper reflection, increased support/motivation, self-efficacy in personal changes, empowered agency, and credence in small acts. However, students also reported reticence and discomfort with teamwork, and apathy and pessimism about society, which undermined their agencies. We therefore recommend strategies to help educators innovate strategies to activate students’ agency, and overcome pessimism and opposition to collaborative and challenge-based learning.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".