Becoming the music teacher: Stories of generalist teaching and teacher education in music
Bibliographic record
Abstract
Teacher preparation for generalist (non-specialist) elementary teachers in Ontario typically comprises one compulsory music class. In entry surveys, many generalists describe themselves as highly apprehensive about and unconfident in their ability to teach music, confirming previous research. Using narrative inquiry, we explore the stories of both preservice and in-service generalist teachers. Data from focus groups with preservice teachers revealed that most participants felt markedly more comfortable with the prospect of teaching music in primary grades after taking our music courses; while fewer expressed confidence in teaching music to junior/intermediate grades. However, preservice teachers are rarely given the chance to observe or teach music while on practicum. Interviews with in-service generalist teachers revealed that a lack of confidence compelled them to take part in intentional, incremental, self-directed learning experiences to further develop their knowledge and skills for the elementary music classroom. The pandemic largely halted music activities, creating new barriers for generalist in-service teachers who teach music. The enduring gaps identified by this research are how to build more opportunities for generalist preservice teachers to practice their newly acquired skills while on practicum; and how to better support generalist in-service teachers to acquire additional skills for teaching music once in the classroom.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".