The Use of Learning Activity Packages to Train Basic Brass Instrument Skills with Isan Lullaby Melodies
Bibliographic record
Abstract
The purpose of this study was to examine the effect of a Learning Activity Package (LAP) incorporating Isan lullaby melodies on the development of basic brass instrument skills among music major students. The study involved two groups of participants: 5 expert scholars in music education and learning management, who evaluated the LAP, and 30 students enrolled in a Basic Brass course, who used the LAP during the semester. The main instruments were the Isan Lullaby – music melody sung to send children to sleep in the northeastern Thailand, Learning Activity Package for basic brass skills, consisting of nine structured practice sets focused on various brass playing techniques on Isan lullaby melodies, a brass instrument skill rubric, and an LAP evaluation form. The students’ performances were analyzed using mean scores, standard deviation, process/product effectiveness with 80/80 criteria, and paired samples t-test. The study found that the use of Isan lullabies in the LAP significantly improved the participants’ brass instrument skills, as shown by their progress during practice sessions and post-test evaluations. The structured exercises helped students develop critical skills such as breath control, articulation, and musical expression. The result contributes to the growing body of evidence supporting the use of culturally relevant music, such as Isan lullabies, in Learning Activity Packages to effectively enhance music education and performance skills.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".