Research on Problems in Music Education Curriculum Design of Normal Universities and Countermeasures
Bibliographic record
Abstract
Presently, most students of normal universities know nothing about music knowledge such as music score at all, and have no musical knowledge and culture. This is a common problem existing in many Chinese normal universities. Surveys, however, show the students are fond of and thirst for learning music knowledge. Thus, universities and related institutions have been laying stress on how to cultivate and improve the music knowledge and culture of students of normal universities. In view of this, this paper aims to research the problems in music curriculum design of normal universities, and put forward corresponding countermeasures, with a hope to give assistance to related universities in music teaching. The theory knowledge of music and vocality accomplishments is one of the basic knowledge and accomplishments that modern talents should have. Besides music majors, education majors also should be offered music education, so as to make up the deficiency of musical knowledge and culture of Chinese musical talents, and comprehensively improve students’ musical skills (Kong & Jin, 2014). There are many problems in music education curriculum design of normal universities. About this, this paper aims to analyze the problems, and put forward suggestions on related curriculum design.
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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 | 0.054 | 0.168 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".