Research on the Influencing Factors of High School English Teacher Professional Learning Community Evaluation in Changchun, China
Bibliographic record
Abstract
This research shows the development and influencing factors of high school English teachers’ evaluation of professional learning community in Changchun, China. Followed Olivier and Hipp & Huffma’s research, the teacher professional learning community evaluation questionnaire was developed by the researchers. 422 English teachers in Changchun were invited to participate in the online survey. This study found that the organization characteristics of professional learning communities in this city shared similar characteristics with Western countries at some degree. English perceptions of including the “shared personal practice” and “collective learning and application” achieves the average level in this research. Compared to the western professional learning communities, the Chinese professional learning communities in this study lack of the democratic leadership and shared value and vision attributions. According to the analysis, we find that the Confucian cultural context, teacher’ career development stages, the pressures of national college entrance examination and the systematic teaching training have a significant impact on the evaluation of teacher professional learning communities. Aiming at to build a learning organisation rather than a bureaucratic administration, we suggest that the “shared value and vison” and “shared and supportive leadership” needs more reform in the current professional learning communities. Namely, traditional Soviet model of school-based professional learning communities need to reform toward a learning organisation emphasizing the teaching and learning.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".