From Physical to Virtual: A New Learning Norm in Music Education for Gifted Students
Bibliographic record
Abstract
Music education is a subject that is generally thought to have much physical activity involved. However, virtual learning has been mandatary applied to most schools worldwide due to the COVID-19 pandemic. The landscape of music learning has had to be switched to online distance learning (ODL), where students learn music virtually using technological tools. Gifted students are among those affected by the implementation of music ODL throughout 2020. Thus, the purpose of this study is to identify the effectiveness of music ODL on gifted students’ motivation. The researchers framed this quantitative study by involving 81 secondary gifted students, aged 13 years, from 13 states in Malaysia. The sample was selected through random sampling, and a preexperimental design was applied to conduct the study. Respondents had been exposed to the music ODL intervention for a month. Data were collected through an adapted questionnaire, namely, the MUSIC Inventory, with a five-point scale. Data were further analysed by descriptive and inferential statistics, integrating two-way MANOVA, using SPSS Statistics version 23. Results reveal that an ODL approach to music classes is significantly effective to enhance gifted students’ motivation domains of empowerment, usefulness, success, interest, and caring. Yet, no significant difference was found in gifted students’ genders and locations on the four domains. Different approaches in music teaching could be further explored for music ODL to gifted students in future studies.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".