MétaCan
Menu
Back to cohort
Record W7019518659

How can Educators Motivate and Support Upper-Elementary and Middle School Singers?

2024· article· en· W7019518659 on OpenAlexaff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
Topicstochastic dynamics and bifurcation
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAffect (linguistics)PerceptionRelevance (law)Class (philosophy)Middle classMusical
DOInot available

Abstract

fetched live from OpenAlex

In many upper-elementary and middle school settings, music teachers have difficulty increasing or retaining enrollment numbers and keeping students engaged in classroom activities, particularly when they require singing. This paper provides a literature review of peer-reviewed studies that investigate both the factors that affect student motivation during these years as well as strategies that may increase student motivation to participate in singing. Due to the small number of studies focused solely on middle school students’ motivation to sing, applicable studies that investigate student motivation to engage in general music making have also been included. Researchers have found that physiological changes due to the onset of puberty, social pressures, student beliefs around talent, and student perceptions around the relevance and enjoyability of their school’s musical offerings are the main reasons students lose interest in singing. Providing individualized and targeted vocal support, fostering a supportive learning environment, giving students the opportunity to select repertoire, and increasing the amount of autonomous active tasks in class can help support and motivate students. Further research on the effects of vocal changes during adolescence for transgender, non-binary, and intersex singers and ways in which music teachers can support these students is needed.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.998

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 communication0.0030.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.459
Teacher spread0.356 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicstochastic dynamics and bifurcationFrench-language works237,207