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Record W4386043880 · doi:10.1177/02557614231194073

Technology-enhanced creativity in K-12 music education: A scoping review

2023· review· en· W4386043880 on OpenAlexaff
Chi Kai Lam

Bibliographic record

VenueInternational Journal of Music Education · 2023
Typereview
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCreativitySubject (documents)Empirical researchCreativity techniqueComputer sciencePsychologyMathematics educationPedagogyMultimediaWorld Wide WebSocial psychology

Abstract

fetched live from OpenAlex

This scoping review addresses internationally published empirical studies on the subject of technology-enhanced creativity. The study aims to identify the types of technological tools used to enhance students’ creativity and examine how technological tools can support students’ creativity in K-12 music education. This review selected and analyzed 17 studies published from 1987 to 2022 in peer-reviewed journals using a rigorous five-stage scoping framework. Data extraction and analysis were conducted in Covidence. The results revealed eight types of technological tools used to enhance creativity in the music classroom, in which sequencer software and GarageBand were the most commonly used type of technological tools and applications respectively. Technology’s support for creativity was also discussed from the perspectives of Lubart’s four roles of computers: (a) computer as nanny, (b) computer as pen-pal, (c) computer as coach, and (d) computer as colleague. The results showed a dearth of research on how technology can become students’ partners to help them generate creative ideas. Based on the findings, this review concluded with implications and recommendations for future research.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.171
GPT teacher head0.511
Teacher spread0.340 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations23
Published2023
Admission routes1
Has abstractyes

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