Music performance anxiety and perfectionism: A comparison of in-person and virtual contexts
Bibliographic record
Abstract
During the COVID-19 pandemic, virtual music instruction became commonplace. Considering the exposed nature and intent focus of participants in online video conferencing, we wondered whether students might experience music performance anxiety (MPA) in their virtual classes to a greater extent than in their in-person classes. Furthermore, we were interested to learn whether prior experience on the instrument/voice used in the online class, gender, and features of perfectionism were related to MPA in these two contexts. A total of 85 university music students completed online questionnaires about their experiences in online performance-based classes, including direct comparisons of their MPA when performing in online classes and in-person classrooms and two perfectionism subscales. Results revealed that online class performance evoked significantly higher MPA than in-person classes. Students performing on new instruments reported significantly higher MPA than those performing on familiar instruments. Concern about mistakes was related to MPA in both contexts. Gender differences were noted with regard to relationships between the measures. Concern about mistakes and instrumental experience were significant predictors of MPA in the online classroom. Implications for educators, administrators, and researchers are discussed.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".