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Record W3153380617 · doi:10.24908/iqurcp.11507

“Working in Concert”: Examining Music’s Role in Cross-Curricular Education

2018· article· en· W3153380617 on OpenAlexvenueno aff
Emily Browne

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumPresentation (obstetrics)Subject (documents)Music educationPedagogyMathematics educationProcess (computing)PsychologyTeaching methodComputer scienceMedicine

Abstract

fetched live from OpenAlex

The purpose of this presentation is to investigate the power of the cross-curricular teaching and learning practice while exploring the relationship between music and other subject areas in an educational environment. In recent years, the curriculum of countries such as Sweden, Australia, and the United Kingdom, has placed greater emphasis on utilizing the cross-curricular learning and teaching practice. This progressive pedagogy strives to engage students by connecting different curricular areas within an activity or lesson. Students have the opportunity to engage in a deeper form of learning, applying their knowledge and transferring their skills as they discover similarities between distinct subject areas. Additionally, the cross-curricular practice can attract pupils to disciplines that otherwise might not have held their attention. As everyday life is filled with tasks that draw on multiple subject areas, this teaching approach ultimately enables the educational system to better prepare pupils for their future. However, the cross-curricular practice can pose a challenge to teachers who lack confidence in their knowledge of content across the many curricular areas. This is particularly relevant to music education, as many teachers who do not possess a solid knowledge base of skills and techniques shy away from incorporating music into their teaching practice. Nevertheless, cross-curricular learning seems to be a valuable learning process, therefore, I will discuss method of incorporating music into cross-curricular lessons in order to provide

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient 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 score0.472
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.228
GPT teacher head0.386
Teacher spread0.158 · 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 designTheoretical or conceptual
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

Citations0
Published2018
Admission routes1
Has abstractyes

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