Where’s the fun in that? Building an authentic, inclusive, serious-yet-playful learning development framework
Bibliographic record
Abstract
At the University of the West of Scotland, one of the ways in which first year students engage in exploring academic, personal, and professional literacies is through an embedded and contextualised credit-bearing module that runs alongside subject specific study. The module offers students opportunities to explore aspects of identity, values, and motivations as part of long, thin, scaffolded engagement running over two terms, and provides a mutable space for student-led discussion on a breadth of aspects of longitudinal transition support, becoming, as well, a space to engage with learning development principles and practices. In so doing, learning experiences within the module are at once guided and exploratory, presenting a risky safe space (Boyd, Wilson and Smith, 2023) for students to use in their transition towards increased autonomy and confidence. This session considered how the flexibility of the learning and teaching spaces created in the module (physical and virtual) allows for both reinforcement of formal structures/ requirements such as assessment processes (the serious part) as well as the freedom to negotiate personalised, aspirational, agentic, experimental learning experiences (more playfully intended). The session shared examples of classroom activities and presented learner feedback. Delegates were invited to share reflection on their own experiences of designing and delivering similar experiences and contribute to a fuller understanding of the value of maintaining balance within the serious-play spectrum.
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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 | 0.011 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.004 |
| 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".