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Record W4416598747 · doi:10.1002/curj.70013

Exploring university student perspectives of a challenge‐based curriculum

2025· article· en· W4416598747 on OpenAlexaff
Miles Thompson, Alpesh Maisuria, Shona McCartney

Bibliographic record

VenueThe Curriculum Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsEducation and Early Childhood Development
FundersUniversity of the West of England
KeywordsCurriculumVariety (cybernetics)ExistentialismPower (physics)Qualitative researchStudent engagement

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0170.012
Scholarly communication0.0240.007
Open science0.0030.019
Research integrity0.0050.010
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.023
GPT teacher head0.238
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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