Experiences of creating digital content for teaching and learning through working in staff-student partnerships
Bibliographic record
Abstract
Most staff-student partnerships are carried out between students who are studying on the course the academics are teaching; this study is different in showing how skilled students work in partnership with academic staff across the university to create digital content for teaching. The students work as digital media producers (DMPs) within the institution, delivering expertise in the creation of digital content working alongside the academics who develop the subject content. This qualitative study had a small, purposive, and selected sample. Academics and the DMPs undertook focus groups to gather insights into their experiences of transforming teaching and learning into a collaborative process. Thematic data analysis and coding were utilised to generate themes, with consideration to the research objectives while analysing the transcripts. Themes appeared, such as having a central support to encourage relationships and build upon skillsets, thereby supplying the students with an authentic experience and aiding their future employment prospects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".