A Thematic Review of the Studies on the Music Teacher Competencies in Turkey
Bibliographic record
Abstract
The first official works about the teacher competencies in Turkey were began in 1998 and many revisions made since then. In this framework, subject-specific competencies were prepared for some areas for primary education in 2008 and secondary education in 2011. This research aimed to review and analyze studies about music teacher competencies between 2008 and 2017 when music teacher competencies were in effect. For this purpose, the studies conducted during this period in Turkey were scanned, and the thematic content analysis was carried out within the framework of the method/design, sample group, data collection tools, aims, results, and recommendations. One of the research findings showed that the studies concerned were particularly related to the competency areas of planning and regulation, the theoretical-applied knowledge and skill, and professional development. Another finding pointed out that the vast majority of the studies employed a descriptive survey model. Last but not least, it was found that the samples of the studies were mostly constructed from pre-service teachers rather than in-service teachers. This current research suggests that further studies should give priority to choose in-service teachers as samples rather than pre-service teachers and also suggests more functional courses for both undergraduate program and in service training.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".