The Development of the Chinese Music Education History Course in the Curriculum of Normal Universities in the People’s Republic of China
Bibliographic record
Abstract
This research aims to investigate the significance and background of the Chinese Music Education History course in normal universities in the People’s Republic of China, develop the course curriculum for these universities, and implement and evaluate the developed curriculum within their academic context. Employing a mixed-methods approach, the study utilized field data collection and expert interviews to explore the current state and institutional context of the course. Based on these findings, a curriculum was designed and subsequently implemented, after which a questionnaire survey of 15 students was conducted to collect feedback and identify necessary revisions. Five experts were then invited to evaluate the curriculum’s structure and content. Data collection instruments included interviews, the draft curriculum document, and evaluation questionnaires. The research confirmed the significance and background of the course through policy analysis, interviews, and surveys, highlighting its importance in music teacher education while identifying practical challenges and proposing solutions. In response to these issues, a structured curriculum was developed, incorporating key components such as course nature, credits, class hours, scheduling, description, objectives, content modules, assessment methods, learning outcome evaluation, and recommended teaching materials. After implementation and evaluation, data from the pilot phase were used to refine the final version, which now serves as a reference for offering the course as an elective in undergraduate music education programs at normal universities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".