Factors Related to Musical Dictation Teaching Habits to School-AgedChildren Among Independent Music Teachers
Bibliographic record
Abstract
Many school-aged children learn music in studio settings, and those lessons often include musical dictation. Nevertheless, we conducted most research about dictation w among collegelevel students. Therefore, we do not know how independent music teachers experience dictation with children. In this paper, we addressed four questions: (1) What are the sociodemographics of teachers who include dictation to their lessons? (2) Why do some teachers choose not to include musical dictation? (3) How often do teachers use strategies when teaching dictation? (4) Are there factors related to the use of those strategies? To get a portrait of the situation, we sent an online questionnaire to studio teachers working with children between 6 and 12 in the Province of Quebec, Canada. We asked them about their instrumental and aural skills teaching habits and their sociodemographic characteristics. Results show that dictation teaching is more common among piano teachers, more experienced teachers, and teachers affiliated with an examination board. We also discovered that the main reason to omit dictation is lack of time. Finally, we found that some strategies are more common among specific categories of teachers. In conclusion, we suggest studio teaching tradition could have a role to play in teachers' decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".