Exploring university student perspectives of a challenge‐based curriculum
Bibliographic record
Abstract
Abstract The world faces multiple global and local challenges, with some describing one challenge, climate breakdown, as an existential threat. Publications in this journal have highlighted the importance of curricula that help students better understand and address these challenges. Delivering more challenge‐based learning experiences may require changes at multiple levels, but as an initial step, this research gathered preliminary data as part of an aspirational co‐design process. Importantly, students were a key part of the research team as co‐researchers, and data were collected from student participants. Using mixed methods, the study explored: (i) how important students feel specific challenges are; (ii) if they feel their current curricula help them navigate these challenges; (iii) whether they would like to have optional challenge‐based learning; and (iv) how this learning could be delivered. In more detail, 61 students from one UK university rated and commented on 30 challenges from existing peer‐reviewed research. While all 30 challenges were, on average, rated as important, the challenges rated as most important concerned: (i) mental health and well‐being; (ii) prejudice, intolerance, and inequality; and (iii) the climate and wider ecological emergencies. However, students were less sure that their current curricula helped them understand and tackle these challenges, and so, perhaps understandably, wanted further learning opportunities. Qualitative data showed a wide variety of views on what format this additional learning should take—but little to no consensus. The discussion considers the tensions inherent in these results, especially in terms of addressing power and politics, and potential issues this may pose both for students and universities operating in an increasingly market‐led and polarised environment. The paper concludes with four tentative recommendations for researchers, funders, leaders, policymakers, and parliamentarians who seek to make a more challenge‐based curriculum a reality.
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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 | 0.020 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.024 | 0.007 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.005 | 0.010 |
| 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".