An Experiential Learning of a Philosophy of Music Education Inspired by the Work of Canadian Composer R. Murray Schafer
Bibliographic record
Abstract
Experiential learning is an educational approach that has been associated with different fields including music education, but rarely with philosophy. Our project consisted of a philosophical experience in action using the work of the Canadian composer R. Murray Schafer. In his Soundscape concept, all sounds in an environment become part of the music that surrounds us. Pre-service student teachers were introduced to his philosophy of music education through experiential learning rather than through a traditional lecture. Additionally, we followed three of them as they taught grades 3, 9 and 11. Our goal was to see to what extent experiential learning of philosophy could be an appropriate pedagogical tool in higher education. Our research question was: How can student-teachers construct their own understanding of a philosophy of music education after having experienced it from the perspective of a student and of a teacher? The following data were examined through collaborative thematic analysis of 1) an open question, 2) their own music composition following Shafer’s guidelines, and 3) their experience of teaching the children. Participants were able to explain in their own words the main components of Shafer’s view on music education, they described how they could use this vision in their own teaching and they identified specific outcomes (creativity, freedom, motivation and critical thinking) from using this approach. The conclusion was drawn that the experiential learning framework can be an appropriate tool for instructing topics that have traditionally been seen as purely theoretical.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.031 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".