Co-design of educational social games with newcomer children: a case study of arabic-speaking migrant tweens
Bibliographic record
Abstract
This article addresses the problem of newcomer children’s social adjustment through the use of educational games and co-design activities. While the adjustment process has been studied broadly, there are few studies on using computer games as a solution, especially the involvement of the children in the design process. This article is based on data collected from two studies aimed at understanding newcomer children’s social adjustment needs and involving them in designing game-based solutions. In the first study and as a pre-requisite to designing any effective solution, we conducted a survey of Arabic-speaking newcomer tweens (9–12 years old), parents, and teachers to understand and contextualise the social adjustment problems the children are experiencing. In the second study, we ran a series of co-design workshops and interviews to investigate what the children learn and which game-play ideas and features they use to promote solutions to the social adjustment problems identified in the first study. Corresponding to our two studies, this article has two main contributions: (1) identifying and confirming the most pressing social adjustment problems, and (2) offering new insights on running co-design workshops with children and the features and ideas that can be used in social adjustment games for newcomer children.
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 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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".