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Record W3047947179 · doi:10.1177/1046878120944197

Challenges in Serious Game Design and Development: Educators’ Experiences

2020· article· en· W3047947179 on OpenAlexaff
Anastasia Dimitriadou, Naza Djafarova, Ozgur Turetken, Margaret Verkuyl, Alexander Ferworn

Bibliographic record

VenueSimulation & Gaming · 2020
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsCentennial CollegeToronto Metropolitan University
Fundersnot available
KeywordsGame designGame design documentGame DeveloperUsabilityGame testingVideo game developmentGame art designKnowledge managementObjectivity (philosophy)Computer sciencePsychologyMultimediaHuman–computer interaction

Abstract

fetched live from OpenAlex

Background. Relatively little is known about the role of educators in serious game design and development and their experiences with serious game implementation. We investigate educators’ perceived challenges deriving from their involvement as subject matter experts during the serious game development trajectory. Methods. A secondary analysis of data collected through an exploratory survey about serious game design and development approaches was carried out. The sample included 41 educators from post-secondary education institutions across North America. An in-depth analysis of qualitative data revealed educators’ roles in game development, the challenges they faced, and the strategies they deployed in serious game design, development, and implementation. Results. Educators, as serious game designers, perceived challenges to be administrative, design-related, attitudinal, and communicative. Strategies deployed to overcome challenges during the concept development, pre-production, and production stages of game design include the creation of games that balance learning and fun, and enhanced team collaboration through cultural mediation. During the post-production stage, although challenges are acknowledged and some strategies, such as improving the usability of a game, are deployed, a clear pattern in challenges and mitigating strategies could not be observed. Conclusion. Serious game design and development can be improved by nurturing diversity of ideas and adopting creative design and development methodologies. Serious game implementation can be improved by devising effective administrative and attitudinal strategies, and incorporating diversity of ideas into target curricula. Additionally, clear directives about usability should be devised, and academic objectivity towards serious games needs to be be created. Strategies to achieve these goals should focus on developing trust between target users, the technical development team, and educators as serious game implementers.

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 imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.025
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0060.005
Open science0.0020.008
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.168
GPT teacher head0.372
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations117
Published2020
Admission routes1
Has abstractyes

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