A Pedagogy of Play: Reasons to be Playful in Postsecondary Education
Bibliographic record
Abstract
Background: Teaching experientially in postsecondary education has challenges; institutional constraints, neoliberal management, and a colonized learning environment. We discuss playing as a form of experiential education. Purpose: We challenge conventional teaching and offer an alternative to enrich and broaden conventional pedagogies. We argue for the benefits of playfulness and how this leads to creativity, wellness, and improved graduate employability. Methodology/Approach: As provocation to the consequences of neoliberalism in education, we examine the literature from a biased position as advocates of play and experiential education. We argue for faculty to adopt an ontology and pedagogy of play. Findings/Conclusions: Play is well represented in the literature; contributing positively to a range of health and educational outcomes. As play manifests in numerous forms in postsecondary education, faculty would benefit from a clear educational rationale for an ontology and pedagogy of play. We share examples from our practice which highlight spontaneous and planned play and playful attitudes/behaviors and suggest how play may be integrated as planned curriculum. Implications: Ideally, these concepts resonate with faculty allowing them to challenge conventional pedagogies and confirm play in practice with the underpinning of experiential education research.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| 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".