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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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; both teacher heads agree on what is shown here.
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".