A Supervision Model for Enhancing Communicative English Teaching and Learning Management for Teachers in Educational Opportunity Expansion Schools
Bibliographic record
Abstract
This study aimed to develop and evaluate a supervision model to enhance communicative English teaching and learning management for teachers in educational opportunity expansion schools under the Office of the Basic Education Commission. The research was conducted in two phases. Phase 1 involved the development of the supervision model through case studies of three exemplary schools with best practices and expert panel discussions with nine purposively selected specialists to assess the model. Phase 2 focused on implementing the developed supervision model. Research instruments included assessment, interview, and seminar recording forms, with data analyzed using mean and standard deviation. The research findings revealed that: 1) The supervision model designed to enhance communicative English teaching and learning management for teachers in educational opportunity expansion schools under the Office of the Basic Education Commission comprised four developmental aspects: 1) planning supervision; 2) implementing supervision; 3) evaluating supervision; and 4) applying supervision. The development process required 120 hours. Experts rated the model’s appropriateness, feasibility, and utility at the highest level. 2) The evaluation of the model’s implementation showed that supervisors acquired greater knowledge and understanding, enabling them to effectively guide target teachers in teaching English for communication. Teachers who received supervision demonstrated improved use of technological tools in their teaching, leading to enhanced communicative English skills among students. Furthermore, supervisors expressed high satisfaction with the model. Overall, the evaluation of the supervision model was rated at the highest 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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".