MétaCan
Menu
Back to cohort
Record W4391171644 · doi:10.1080/0144929x.2024.2307449

Co-design of educational social games with newcomer children: a case study of arabic-speaking migrant tweens

2024· article· en· W4391171644 on OpenAlexaff
Omar Bani-Taha, Ali Arya, D. R. Fraser Taylor

Bibliographic record

VenueBehaviour and Information Technology · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsCarleton University
Fundersnot available
KeywordsArabicPsychologyProcess (computing)Social integrationSocial psychologyMathematics educationSociologyComputer scienceLinguistics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.367
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.303
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBehaviour and Information TechnologySame topicChild Development and Digital TechnologyFrench-language works237,207