Bibliographic record
Abstract
Background We contend that a conceptual conflation of simulation and imitation persists at the heart of claims for the power of game-based simulations for learning. Recent changes in controller-technologies and gaming systems, we argue, make this conflation of concepts more readily apparent, and its significant educational implications more evident. Aim This article examines the evolution in controller technologies of imitation that support players’ embodied competence, rather than players’ ability to simulate such competence. Digital gameplay undergoes an epistemological shift when player and game interactions are no longer restricted to simulations of actions on a screen, but instead support embodied imitation as a central element of gameplay. We interrogate the distinctive meanings and affordances of simulation and imitation and offer a critical conceptual strategy for refining, and indeed redefining, what counts as learning in and from digital games. Method We draw upon actor-network theory to identify what is educationally significant about the digitally mediated learning ecologies enabled by imitation-based gaming consoles and controllers. Actor-network theory helps us discern relations between human actors and technical artifacts, illuminating the complex inter-dependencies and inter-actions of the socio-technical support networks too long overlooked in androcentric theories of human action and cognitive psychology. Conclusion By articulating distinctions between simulation and imitation, we show how imitative practices afforded by mimetic game controllers and next-generation motion-capture technologies offer a different picture of learning through playing digital games, and suggest novel and productive avenues for research and educational practice.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.020 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".