Well, DAW! That’s Why I Don’t Sound Like the Recording: Music Production in Elementary School Music Education
Bibliographic record
Abstract
In this dissertation, I discuss how elementary students responded to a music curriculum that foregrounds music production practices. The following question guided this inquiry: 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? I theorized that music production practices should be introduced at an earlier stage of learning than discussed by other educators, i.e., at the secondary level. The study consisted of adapting, implementing, and reflecting on a music production curriculum in public elementary school music education through participatory action research, alongside three other music teachers, their students, and two prominent Canadian music industry experts such as recording engineers. The study was adapted in three phases; phase one consisted of music industry experts informing the content of the project; phase two consisted of teacher planning and implementing a flipped classroom design; and, phase three included gathering student input and experiences, leading to the creation of open access video resources. The videos are free and available publicly as an animation series on YouTube called “Dr TooNice.” Specific to this research, North American, or in particular, Canadian public school music education has traditionally focused on instrumental and vocal performance-based programs by using approaches such as Kodály, Orff, and Dalcroze with a limited focus on music production techniques. These techniques are pertinent to students interested in performance-based and non-performance-based settings, underscoring their significance in diverse contexts and environments. To obtain the quality of the sonic features heard on most recordings requires a specific skill set beyond performance. Educators turning to contemporary popular music for song selections are often unaware that they are expecting students to recreate sounds on instruments that have been altered through music production techniques. Understanding what one is hearing, and knowing how to create and recreate these sounds, is a crucial element missing in public school music education. The results of my research all lead to the recommendation of beginning music production at an early stage of learning, including, but not limited to, the processes of 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 | 0.003 | 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 | 0.008 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".