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Record W2990702255 · doi:10.1109/tg.2019.2954880

A Game Design Plot: Exploring the Educational Potential of History-Based Video Games

2019· article· en· W2990702255 on OpenAlex
Farzan Baradaran Rahimi, Beaumie Kim, Richard Levy, Jeffrey E. Boyd

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Games · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCasualComputer sciencePlot (graphics)Video game designGame mechanicsVideo gameTurns, rounds and time-keeping systems in gamesMultimediaGame designGame Developer

Abstract

fetched live from OpenAlex

The number of video games that are developed based on real historical events and evidence is increasing. These history-based video games provide learning opportunities to players, but a certain type of such games-first- and third-person shooters-has not been carefully examined for their potentials. Knowing what players say about their game experience-even if the information and knowledge are inaccurate-helps researchers understand what type of learning could happen with such games. In this article, we propose a systematic approach to assessing games as learning environments, using the method of comparing the authenticity of popular history-based video games. Through a qualitative data analysis, we studied players' comments on the web-based communication services, such as game forums, digital distribution platforms, and discussion websites. Casual players' conversations on these websites showed that there exist several learning potentials in the games for players, including building their understanding about history and historical forces of the time, through personally relating to specific events, social artifacts, and places.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.048
GPT teacher head0.265
Teacher spread0.217 · 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