Students as partners in e-contents creation: A case study exploring student-staff partnership for learning and student engagement using digital applications for co-creation of e-learning materials
Bibliographic record
Abstract
In order to enable learners to take control of their learning needs and actively contribute in their learning processes, educators can partner with students in various reciprocal student-staff partnership (SSP) settings where students can be co-creators, co-producers, curators, or co-deliverers of the curriculum. Our project, undertaken to enhance the curriculum as part of a teaching qualification, places emphasis on educators partnering with first-year undergraduate students over e-content creation within an existing module, using readily accessible digital applications in order to promote active learning in students and improve student engagement. In this case study, we evaluate the extent to which SSP, as an approach to the creation of e-learning materials using digital applications, enhanced learning and student engagement in an existing module. Our student partners perceived SSP to be an excellent platform for learning, actively engaging in the classroom, and developing skills such as communication and digital literacy. However, they expressed some concerns about overcoming the traditional hierarchies within our SSP initiative
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".