Opinions of University Music Teachers on the Musical Competencies Necessary for Primary Education Teachers
Bibliographic record
Abstract
This paper describes the research conducted in the Republic of Croatia during the 2012/13 academic year. We have gathered opinions from experts, i.e. teaching methods teachers from seven faculties of teacher education, regarding the music teaching competencies necessary for primary education teachers teaching music in the first several grades of elementary school. We used the Delphi method in our research, i.e. in our sample survey among teaching methods teachers. The teachers also evaluated the competencies of their students and some elements of teacher education studies course syllabi and programmes. The sample survey among the teachers was implemented via email. The goal of the research was to determine if the programmes of the music courses at the teacher education studies are appropriate for the development of the competencies necessary for students of music education. Teaching methods teachers emphasized the need for more practical training, primarily regarding playing instruments and singing, and they pointed out that the course Teaching Methods in Music is the most important course for the training of future music teachers. Aside from that, they believe that more classes should be devoted to music courses, i.e. they propose to reorganise the contents of the courses by increasing the amount of practical classes and reducing the amount of theory classes. They also believe that it is necessary to introduce testing of musical ability at entrance exams for admission into the teacher education studies.
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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 | 0.003 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".