Ludic Pedagogy: Taking a serious look at fun in the COVID-19 classroom and beyond
Bibliographic record
Abstract
The COVID-19 pandemic has affected deep reflection in higher education classrooms: how do we attract and retain students to (temporary but nevertheless increasing) online learning experiences, how do we keep them at our universities and colleges, and how do we give students a learning experience from which they will remember meaningful information? In this paper, we introduce a new pedagogical framework that we call Ludic Pedagogy. We address the four elements of this model: fun, positivity, play, and playfulness. Each of the elements is described in turn, together with literature outlining how each contributes to a positive classroom environment that helps students engage with and learn course content. Examples of how the authors have used this pedagogical model are included and described. We suggest that instructors consider using the Ludic Pedagogy model so as to improve engagement, learning outcomes, and retention in their classes and broader university/college contexts.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| 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".