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Record W2785155905 · doi:10.5539/jel.v7n1p299

An Investigation of Music Teacher Candidates’ Performance Anxiety Levels in Piano Examinations

2018· article· en· W2785155905 on OpenAlexvenueno aff
Deniz Beste Çevik Kılıç

Bibliographic record

VenueJournal of Education and Learning · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicDiverse Music Education Insights
Canadian institutionsnot available
Fundersnot available
KeywordsPianoAnxietyPsychologyAffect (linguistics)Music educationDevelopmental psychologyClinical psychologyPedagogyCommunicationArtPsychiatry

Abstract

fetched live from OpenAlex

Examination anxiety in piano education, one of the important courses in music education, can negatively affect both success in examinations and the education of students. This study aimed to determine the anxiety levels of students in the music education departments of universities in western Turkey regarding their piano examinations and their significance according to different variables. The study sample consisted of 174 female and 107 male students. The study found the students’ performance anxiety levels related to piano examinations to be moderate. There were no statistically significant differences in their anxiety levels by gender or age. Students with their own pianos were less likely to have performance concerns regarding piano examinations than those without. It was also found that the students’ year of study had a significant effect on their performance anxiety levels in piano examinations.

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)0.001
metaresearch head score (Gemma)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.055
GPT teacher head0.274
Teacher spread0.219 · 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

Citations11
Published2018
Admission routes1
Has abstractyes

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