Relationship Between Arts Education Course System of Advanced Normal University and Arts Course of Middle School in China as Well as an International Comparative Study
Bibliographic record
Abstract
With the coming and development of the global knowledge economy in 21st century, social development and economic growth are more and more relying on knowledge and talents. As an important approach of intermediate level arts teachers training, arts education in advanced normal universities is bearing the educational responsibility of training middle school arts teachers with solid professional abilities, broad knowledge and talent in different aspects. In some of countries with developed education in the world, such as America, Britain, France, Germany, Japan, Singapore, etc., the training of arts teachers is performed by comprehensive universities, while in China, the arts education system of advanced normal universities is mainly established centering on the professional arts college, which places emphasis on teaching of professional knowledge in both course setting and teachers’ arrangement of teaching content and ignores training on students’ education ability, resulting that arts graduates don’t have necessary comprehensive quality. As a result, arts education in middle school cannot be comprehensively performed. How to accommodate arts education course setting in advanced normal universities to the system of arts education in the middle school with a reference to advanced educational experience in developed countries has become a main theme we have to discuss.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".