Montage, DaDa and the Dalek: The Game of Meaning in Higher Education
Bibliographic record
Abstract
This paper argues for visual playfulness in Higher Education learning and teaching practice. We offer a case study example of how we, the authors of this paper, have incorporated creativity into our teaching - the Facilitating Student Learning module, the first module in the Postgraduate Certificate in Learning and Teaching in Higher Education. We outline how we used ‘visualising to learn’ and what learning resulted from our visualisation practices. With our staff learners, we found that visual play gave them the freedom to experiment, to question and to progress; important in these supercomplex, uncertain times. Our desire was not to ‘fix’ or train academic staff, but to give them the space and tools to become liberatory professionals on their own terms and in their own ways so they can support their students to also become academic without losing themselves in the process. We propose that what is needed are methods and methodologies that enable learners - staff and students - to evolve and transform as they co-construct their knowledge in ludic ways. We incorporate images of the representations that our participants have made of themselves, of their students and of Higher Education systems to illustrate the challenges and possibilities of visual learning - and of creative staff development practice in general - and invite the reader to engage dialogically with them also to see what meanings they might make of them.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.024 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".