How to make the charm of theatre shine in schools? ——Investigation and reflections on the implementation of theatre education under the “New Arts Curriculum Standards” in mainland China
Bibliographic record
Abstract
With the release of the Arts Curriculum Standards for Compulsory Education (2022 Version), significant progress has been made in theatre education at the compulsory education stage in China. The penetration and participation rates of school theatre courses have steadily increased, along with the growth and professionalization of the teacher workforce and the enrichment of course formats, teaching content and methods. Students have shown high levels of interest and engagement. However, the implementation of school-based theatre education still faces numerous challenges, including imbalances in teacher’s gender and professional titles, insufficient course time, inadequate teaching resources, underdeveloped mechanisms for managing theatre clubs, and disparities in students’ foundational abilities and insufficient external support. Further analysis reveals deeper constraints hindering the widespread adoption of theatre education: the lack of teacher certification and clear standards for career advancement, misalignment between teacher education programs and school needs, unestablished collaboration mechanisms with social arts troupes, and regulatory challenges in the off-campus training market. To promote the comprehensive development of theatre education in the future, it is recommended that the government improve teacher certification and career advancement systems, higher education institutions adjust teacher education programs and enhance professional development for in-service teachers, and social arts troupes and off-campus training vendors strengthen their collaboration with schools. These efforts aim to establish a sustainable support mechanism for the development of theatre education.
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 | 0.004 | 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 | 0.008 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".