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Record W2020405287 · doi:10.1504/ijart.2013.055391

A flash-based on-the-job training game

2013· article· en· W2020405287 on OpenAlex
Eduardo Augusto Werneck Ribeiro, Maiga Chang

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

VenueInternational Journal of Arts and Technology · 2013
Typearticle
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsAthabasca University
Fundersnot available
KeywordsGame DeveloperVideo game designGame designComputer sciencePresentation (obstetrics)Video game developmentGame mechanicsNon-cooperative gamePhoneGame design documentMultimediaMode (computer interface)Human–computer interactionGame theoryEconomicsMicroeconomics

Abstract

fetched live from OpenAlex

Most of research results show that the educational games are good to increase student’s learning motivation in formal learning. This research reveals the project of designing and implementing a web-based educational game within a real corporate environment. It is amazing to find that what a real world company looks for is such a small and simple game. The game has been approved by the officer and used to provide to phone and face-to-face sales who have no minimum necessary knowledge of selling watercrafts insurance and have interest in using game-based learning mode instead of traditional PowerPoint presentation mode. The results show that the game mode’s dispersion is quite high, showing a more volatile situation. Good news is, although the employees did not like the game itself, they are still willing to try the game, instead of the presentation, if offered.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
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 head0.032
GPT teacher head0.318
Teacher spread0.285 · 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