Improving Engagement & Inclusive Pedagogy Within High-School Music Education
Bibliographic record
Abstract
Music education has the opportunity to give many mental, cognitive and social benefits to students, especially those in high school who may find themselves navigating challenges associated with adolescence. However, many students aren’t continuing to pursue music education post-grade nine, thereby limiting exposure to these unique benefits. Despite the strong intent of teachers to support their students, opportunities may be missed for teachers to truly understand what motivates their students to stay connected to music. My research aims to fill the gap in Ontario high school engagement research by exploring the engagement needs and trends across a schoolboard in Niagara. In my SSHRC-funded qualitative study, instrumental music classes from a variety of grade levels were observed, and then each participating educator was interviewed to learn more about their experiences. This work explores what motivates students to participate in, and stay engaged with, music and music education. This research is framed using a Self-determination Theory lens. Findings suggest that teachers use a wide range of strategies to engage their students, and that their role is to shape the classroom around student needs and grow their initial engagement through teaching according to their class's specific needs. The results implicate teachers to learn from their students’ behavioral and emotional reactions to content, and guide how they support their students based specifically on unique needs over only using generalized strategies to estimate their needs
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.007 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".