Designing Game-Centred Curricula: A Critical Inquiry
Bibliographic record
Abstract
Background: Digital games as technologies for teaching and learning are finding their way into schools with increasing frequency, raising questions about how teachers plan for their use. Aim: This paper utilises curriculum inquiry to explore the experiences of teachers designing curricula that centre digital games for play and study. Methods: We employ a memory work methodology to analyse four English teachers’ reflections, emphasizing the value of reflecting on everyday actions to understand the complexity of professional lives and the situated nature of knowledge. Results: Our paper reveals that designing and implementing digital game-centred curricula is complex. The analysis of themes related to engaging with students’ lifeworlds, planning for skills and knowledge, the challenges of play, and issues of access and equity, suggest use of technology for school learning is always inseparable from other phenomena, such as teaching methods, purposes, values and contexts. Conclusion: Those engaged in the design of game-centred curricula are in a constant state of negotiation which neither starts nor ends with the production of material artefacts.
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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 | 0.036 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".