Well DAW! That’s Why I Don’t Sound Like the Recording: Teacher Perspectives on Music Production in Elementary Schools
Bibliographic record
Abstract
The study was aimed to adapt, implement, and reflect on a music production curriculum in public elementary schools while developing an open-access resource for teachers. Two music industry professionals informed the content of the study; four elementary school teachers, including myself, implemented the curriculum; and 28 students from primary, junior, and intermediate grades applied the concepts in various projects. Using participatory action research (PAR) as a methodological framework, I explored the following research question: How can music industry professionals, music teachers, and students collaborate, share ideas and their experiences, to inform a curricular design for public elementary school music education that has music production as its core? I theorized that music production should be introduced earlier than traditionally discussed, at the elementary rather than secondary level. The study followed three phases; (1) music industry professionals informed content development: (2) teachers planned and implemented a flipped classroom approach; and, (3) student feedback and experiences guided the creation of open access video resources. Findings support the early integration of music production in elementary education, emphasizing, but not limited to, vocal mixing, beat making, and recording with digital audio workstations (DAWs).
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".