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Record W1946908127 · doi:10.21432/t26g7b

Implementing Game Design in School: A Working Example | Mise en œuvre de la conception de jeu à l’école : un exemple pratique

2015· article· en· W1946908127 on OpenAlexvenueno aff
Dani Herro

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2015
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)SociologyCurriculumHumanitiesPedagogyArtGeography

Abstract

fetched live from OpenAlex

This case uses a worked or “working example” model (Gee, 2010), documenting the implementation of a novel game design curriculum in the United States. Created by an Instructional Technology Administrator (ITA) and two classroom teachers, it was subsequently offered to high school students. With an aim of providing in-depth understanding of conditions necessary to bring game design experiences to classrooms, the research describes the context while revealing processes and instructor perceptions of the experience. Data collection and analysis in this working example include observation, teacher interviews, student surveys, and artifacts intended to make thinking and practices overt while inviting scholarly conversation around the curriculum’s successes and failures. Drawing on a previous case focused on initial course planning and early implementation (Author, 2013), this paper advances insight regarding the process of moving game design into schooling and concludes with a discussion of educational implications. Cette étude se sert d’un modèle « d’exemple concret » (Gee, 2010) pour documenter la mise en œuvre d’un programme de conception de jeu aux États-Unis. Créé par un technopédagogue et deux titulaires de classe, le programme a ensuite été offert à des élèves du secondaire. Visant à fournir une compréhension approfondie des conditions requises pour l’intégration des expériences de conception de jeu en classe, l’étude décrit le contexte et révèle les processus et les perceptions qu’a tirés l’instructeur de l’expérience. La collecte de données et l’analyse dans cet exemple concret comprennent l’observation, des entrevues avec les enseignants, des sondages auprès des élèves et des artefacts ayant pour but de rendre manifestes la réflexion et les pratiques tout en stimulant la conversation savante à propos des réussites et des échecs du programme. S’appuyant sur une étude préalable qui mettait l’accent sur la planification initiale du cours et le début de sa mise en œuvre (Herro, 2013), cet article propose une perspective sur le processus d’intégration de la conception de jeu dans la formation et conclut avec une discussion portant sur les répercussions pédagogiques.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.737
Threshold uncertainty score0.717

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.313
Teacher spread0.282 · 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

Citations4
Published2015
Admission routes1
Has abstractyes

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