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Record W2593079331 · doi:10.1386/jmte.9.3.273_1

So you think you can play: An exploratory study of music video games

2016· article· en· W2593079331 on OpenAlex
Jen Jenson, Suzanne de Castell, Rachel Muehrer, Milena Droumeva

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

VenueJournal of Music Technology and Education · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsSimon Fraser UniversityYork University
Fundersnot available
KeywordsMusicalGuitarLEAPSHEROMultimediaProgrammingPsychologyDigital audioVisual artsComputer scienceMusical compositionArtAcousticsSpeech recognitionArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Digital music technologies have evolved by leaps and bounds over the last 10 years. The most popular digital music games allow gamers to experience the performativity of music, long before they have the requisite knowledge and skills, by playing with instrument-shaped controllers (e.g. Guitar Hero, Rock Band, Sing Star, Wii Music ), while others involve plugging conventional electric guitars into a game console to learn musical technique through gameplay (e.g. Rocksmith ). Many of these digital music environments claim to have educative potential, and some are actually used in music classrooms. This article discusses the findings from a pilot study to explore what high school age students could gain in terms of musical knowledge, skill and understanding from these games. We found students improved from pre- to post-assessment in different areas of musicianship after playing Sing Party , Wii Music and Rocksmith , as well as a variety of games on the iPad.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.174
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.045
GPT teacher head0.253
Teacher spread0.208 · 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