Virtual Concerts in Learning Oboe-Played Chinese Folk Music: Impact on Performance Proficiency, Perceived Aesthetic Qualities, and Students’ Motivation
Bibliographic record
Abstract
This study compares the effectiveness of video communication technologies and group chats in virtual reality (VR) as platforms for practising musical skills among students. Additionally, it examines the capacity to convey aesthetic characteristics of musical performance through these two forms of remote communication and the influence of these technologies on student motivation. The research involved 106 senior students from two higher educational institutions in China. Zoom and VRChat served as the instructional platforms for the two experimental groups. The findings did not reveal significant differences in performance mastery. Specifically, the perception of task value demonstrated the most substantial increase, scoring 5.65 compared to 4.81 out of a possible 7; all three pairs of values exhibited significant differences between the groups based on the results of the student’s t-test. Furthermore, a significantly higher sense of immersion and quality of aesthetic experience was observed within the VR group, scoring 4.81 compared to 3.70 out of a possible 5 in the videoconferencing group. Additionally, VR’s greater capability to convey characteristic emotional nuances of music was confirmed by the fact that within the VR group, two out of six distinctive features of Chinese folk music (lyrical, highly artistic aspects and intonation subtlety) were more pronounced than in the videoconferencing group. These results indicate the potential of VR technology to enhance the quality of aesthetic experience as well as the motivation for learning among students in music education, including those studying wind instruments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".