‘Trying to open the doors’: The co-creation of digital resources for disadvantaged primary school pupils
Bibliographic record
Abstract
This article explores the use of co-creation as an approach for involving university students in the development of educational initiatives for widening participation (WP) in higher education (HE) during the COVID-19 pandemic. At present, research and guidance looking at how co-creation practices can enable the production of such initiatives within HE is highly limited, which can deter others from employing this approach. To this end, we provide a case study of a WP project called Topic in a Box that involved staff and students working together to produce digital learning material for primary schools and students over several months. Through the use of semi-structured interviews with nine students, this research provides insight into the steps that were taken to develop the project, capturing the motivations, benefits and challenges of co-creation practice from a student perspective. We argue that this mode of co-creation should be used to a greater extent across the university sector and in recognition that university students have much to offer in terms of widening access to university.
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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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.024 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.002 | 0.033 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".