Investigation of Music Teacher Candidates’ Motivation and Study Skills in Voice Education Course
Bibliographic record
Abstract
This study aims to reveal the relationship between the motivation and study skills of music teacher candidates regarding the individual voice training lesson and to reveal the relationship between these two variables. Correlational survey model was used as the research method. The population of the research was chosen from one university in seven geographical regions of Turkey, studying in the Music Education Department, using the random cluster sampling method. The sample consists of freshman students of Music Education Department in Turkey. Personal Information Form created by the researcher, “Individual Voice Training Course Motivation Scale” and “Study Skills Scale” were used as data collection tools in this research. In the analysis of the data, the percentages and frequencies of the Personal Information Form are shown in the tables. SPSS 25 package programme was used for data analysis. In the analysis of demographic variables to continuous variables, normality was tested, and t-Test and One-Way ANOVA analysis were used in cases of normality. In the absence of normality, the Kruskal-Wallis H test was used. Spearman Rank Correlation was used to observe the relationship between continuous variables.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".