The Development of a Teaching Guidebook for Tenor Singing of Puccini’s Opera for Chinese Undergraduate Students in Xi’an Conservatory of Music, China
Bibliographic record
Abstract
This study aimed to examine the current context of tenor singing in Puccini’s operas, develop a targeted teaching guidebook, and evaluate its effectiveness for Chinese undergraduate students. The research was conducted in three phases. First, six voice instructors from leading Chinese conservatories were interviewed to ascertain current practices in teaching Puccini arias. In Phase 2, the guidebook was developed based on input from five vocal experts from China and South Korea, incorporating both technical and interpretive strategies. In Phase 3, the guidebook’s content validity, accuracy, and practicality were evaluated through expert reviews and feedback from 16 undergraduate students at Xi’an Conservatory of Music. The findings revealed that although Chinese tenor students show strong interest in Puccini’s arias, participation remains limited due to the technical demands of the repertoire, emphasizing the need for a solid vocal foundation. Accordingly, the finalized guidebook features six carefully selected arias, integrated core vocal fundamentals, strategic technical exercises, performance training, and a structured study plan. After eight weeks of learning cycles, students reported high levels of satisfaction, noticeable improvement in vocal ability, and greater clarity in approaching Puccini’s music. These results affirm the guidebook’s value as an effective instructional resource for enhancing Puccini aria education in Chinese undergraduate vocal programs.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".