Development of Indicators and Approach for Enhancing Chinese Language Teacher Leadership of Secondary Schools in the Northeast
Bibliographic record
Abstract
The leadership of Chinese language teachers holds great importance, as teachers play a crucial role in shaping the future of students and society as a whole. Currently, China recognizes the significance of education management, and as the center of economic and social development in Asia, teacher leadership becomes a vital factor in ensuring quality learning outcomes. This research aims to achieve the following objectives: 1) Explore the components and leading indicators of Chinese language teachers in secondary schools in the northeast region of Thailand; 2) Test the concordance of the measurement model for Chinese teacher leadership; 3) Study the approach for enhancing Chinese language teacher leadership; and 4) Investigate the results of implementing the approach at the secondary school. The research employed a mixed methods approach, consisting of four phases. Descriptive statistics and statistical packages were utilized for data analysis. The results revealed that the leadership of Chinese language teachers at the secondary level comprises four main components and twelve indicators. The measurement model for Chinese teacher leadership demonstrated consistency with empirical data, displaying statistical significance with a P-value of 0.30, RMSEA of 0.033, SRMR of 0.04, CFI of 0.99, and TLI of 0.99. Based on these findings, the approach for enhancing Chinese language teacher leadership, including the following aspects: Change leader: Teachers should possess a systematic operational plan that aims towards desired goals and be adaptable to adjust according to the situation. Self-development: Teachers need to formulate development plans for future goals, acquire knowledge, skills, and academic leadership, and exhibit creative thinking and innovative approaches. Teaching role model: Teachers should foster an environment that encourages freedom of thought, assertiveness, and strong teacher-student relationships. Participation in development: Teachers should collaboratively plan curriculum development, ensuring that teaching and learning meet international standards of academic excellence. They should also encourage multilingual communication and the production of creative work. Finally, the implementation of the guidelines yielded positive results, proving their appropriateness, feasibility, and usefulness across all aspects at a high level.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".