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Record W3170808680 · doi:10.14434/ijdl.v12i1.31263

Hack, Slash & Backstab: A Post-Mortem of University Game Development at Scale

2021· article· en· W3170808680 on OpenAlexaff
Andrew Phelps, Christopher A. Egert, Mia Consalvo

Bibliographic record

VenueInternational Journal of Designs for Learning · 2021
Typearticle
Languageen
FieldComputer Science
TopicTeaching and Learning Programming
Canadian institutionsConcordia University
FundersUniversity of California, Irvine
KeywordsContext (archaeology)Computer scienceDocumentationMultimediaDisciplineSituated learningVideo game developmentSituatedGame designPedagogySociology

Abstract

fetched live from OpenAlex

This article describes the educational, operational, and practical implementation of an upper-division undergraduate studio-style course centered on the subject of game production. Specifically, the article addresses the course organization and processes, the institutional context for the course (i.e., its situated role in the larger curriculum), the overall structure of the course both from a pedagogical and operational point of view, and concludes with substantial reflection and analysis by the authors on what worked effectively and where improvements could be made. The article also provides substantial depth regarding the student experience, the structure of creating muti-disciplinary software development teams within the course, orienting the course around the successful production of a professional-grade XBOX One video game product, and various methods, structures and tools for course organization, communication, software development practice, documentation, etc. This in turn is framed in the larger context of the course as it was offered not only through an academic department, but in parallel with a campus-based games studio and research center. Numerous detailed elements are provided in such fashion as to provide other educators and mentors a relevant, structured, and detailed post-mortem of a large scale, multi-disciplinary effort that engaged students in complex multimedia software production in a professional context. In addition, several elements atypical from more traditional software project courses as they intersect game development including entertainment design, playtesting, marketing, press, public demonstration and performance, audience reception and analytics, commercial platform, etc., and discussed and analyzed.

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)0.002
metaresearch head score (Gemma)0.013
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.003
Scholarly communication0.0040.002
Open science0.0010.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0140.004

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 head0.033
GPT teacher head0.275
Teacher spread0.242 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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