Complexity thinking in PE: game-centred approaches, games as complex adaptive systems, and ecological values
Bibliographic record
Abstract
Background: This article draws on the literature relating to game-centred approaches (GCAs), such as Teaching Games for Understanding, and dynamical systems views of motor learning to demonstrate a convergence of ideas around games as complex adaptive learning systems. This convergence is organized under the title ‘complexity thinking’ and gives rise to a comprehensive model of game-based learning that addresses theoretical and practitioner considerations relevant to researchers and teachers. Complexity thinking is also partnered with an ecological integration value orientation to reinforce the dominant purposes of game-based learning in physical education. Key concepts: The study of game-based learning from a complexity thinking perspective relies on the foundational alignment of game characteristics with those of complex learning systems. Both complex learning systems and games are (a) comprised of co-dependent agents, (b) self-organizing, (c) open to disturbance, (d) sites of co-emergent learning, (e) open to varying experiences or interpretations of time, and (f) able to evolve their structures in response to feedback. Considering games as learning systems opens the door to consideration of the system being as sustainable and adaptable as it can. Sustainability, adaptation potential, and engagement levels emerge from the ‘game as learning system’ discussion in order to provide insight into the functioning of the game. High levels of engagement and sustainability are the presented goals for teachers working from a complexity thinking perspective. A number of key concepts from systems literature, such as attractors, affordances, attunement, and disturbances, are discussed as identifiable and manipulatable dimensions of game-based learning. Implications for the PE profession: Physical educators are well positioned to notice learning as it emerges and to construct environments that focus learning without forcing learning. Complexity thinking concepts such as flow, coupling, engagement, attractors, affordances, attunement, and disturbance, in combination with the pedagogical principles advocated by GCAs, provide a robust set of analytical and teaching tools. It is to be hoped that a deepening of understanding of how game forms and game play lead to learning during games will improve the quality of learning experiences in games and foster increasing and prolonged engagement by students.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".