Peculiarities of the Development of Students’ Musical Skills Under the Influence of Modern Software
Bibliographic record
Abstract
This study explored the impact of digital technologies on the development of musical skills among music students. A learning experiment was conducted with 66 students between the ages of 18 and 21 from China, France, Italy, and Spain. The study used the methods of a survey and online discussions. Participants verified that the present advancement of digital technologies allows artists to participate in a professional musical environment without formal schooling. Students in the experimental group had a more positive attitude toward learning and its significance for their personal and professional development. Most survey items were rated between 3 and 4 on a 4-point scale, indicating students’ overall satisfaction with the training. The results of the online discussion also indicated a high level of support for the use of digital technologies in music education, as well as highlighting the innovative nature of the training course and the advantages of traditional music education. Three quarters of participants supported the use of digital technologies in education. Students in the experimental group were able to acquire more advanced professional musical skills, which facilitated the creation of melodies (including the composition of musical fragments on specified themes, musical arrangements of varying complexity, and the development of principles for processing musical works) compared to students in the control group (focused on the development of musical ear and sense of rhythm), who were initially trained under the traditional system. The study’s findings support the effectiveness of an integrated strategy for nurturing musical creativity that involves collaboration between students, teachers, and cutting-edge technology.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".