The Development of The Brain-based Learning Instructional Package of Phin Performance for the Isan Folk Music Undergraduates Students
Bibliographic record
Abstract
This study aimed to develop a brain-based learning instructional package for Phin performance based on the teaching techniques of expert Phin masters in the Isan region of Thailand. The research was conducted in three phases: (1) analyzing the instructional methods of eight expert Phin masters through interviews and observations; (2) developing a brain-based instructional package using the data from Phase 1 and validating it with experts in music education; and (3) investigating the effectiveness of the package by comparing the Phin performance skills of undergraduate music students taught using either conventional methods or the developed package. The participants included eight Phin masters, five music education scholars, and ten undergraduate students. The findings revealed that students taught through the brain-based learning package achieved significantly higher performance scores than those taught through conventional methods. The study demonstrates the effectiveness of integrating neuroscience-informed pedagogy with traditional music instruction and provides a model for preserving and promoting local musical heritage through innovative teaching practices.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".