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Record W2741817468 · doi:10.1386/jmte.10.1.31_1

Technical exercise practice: Can piano students be motivated through gamification?

2017· article· en· W2741817468 on OpenAlexaff
Heather J. S. Birch, Earl Woodruff

Bibliographic record

VenueJournal of Music Technology and Education · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPianoStudioContext (archaeology)PsychologyProcess (computing)Game mechanicsMathematics educationControl (management)Applied psychologyMultimediaComputer scienceArtArtificial intelligence

Abstract

fetched live from OpenAlex

Abstract Gamification is a process whereby game design and game mechanics are applied in non-game contexts to influence behaviour. This research study explores the effects of gamification on young piano students’ practice of technical elements such as scales, chords and arpeggios in the context of independent practice between private lessons. A control and a treatment group of ten piano students each were formed across two different private piano studios. A game-like environment was introduced for the treatment group, in which the players experienced game elements such as avatars and rewards, including points, badges and level achievements. Gamification was found to have a positive effect on the number of technical elements students mastered and a modest effect on their attitude towards practicing technical elements. The educational implications for these findings are discussed.

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)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.054
GPT teacher head0.328
Teacher spread0.273 · 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 designObservational
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

Citations29
Published2017
Admission routes1
Has abstractyes

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