A Model for the Development of English for Communication Management at the Primary Level of Private Schools
Bibliographic record
Abstract
This research aimed to: 1) investigate the current situation, desirable conditions, and essential needs for managing English language learning for communication, 2) develop a model for managing English language learning for communication, 3) study the outcomes of using the model for managing English language learning for communication, and 4) evaluate the model for managing English language learning for communication at the primary level of private schools. The research is divided into 4 phases: Phase 1: Investigating the current situation, desirable conditions, and essential needs through a multi-stage random sampling of 174 individuals. Phase 2: Developing a model for managing English language learning for communication, verified by 9 qualified experts. Phase 3: Examining the outcomes of using the model for managing English language learning for communication with a target group of 14 individuals, including school administrators, academic department heads, and English language teachers in private schools. Phase 4: Studying the results of implementing the model for developing English language learning management for communication at the primary level of private schools. The research tools utilized consisted of questionnaires, assessments, in-depth interviews, and tests. The applicable statistics included mean, standard deviation, and analysis of necessary requirements (PNImodified). The research findings indicated that the current overall situation is at the highest level, while the desired situation is also at the highest level. Regarding the level of necessity, it ranked from high to low as follows: English language learning management for communication and education quality in English language learning management for communication. The model comprised six components, namely: 1) Principles, 2) Objectives, 3) Curriculum Content, 4) FSDLP Process Components, 5) Measurement and Evaluation, and 6) Conditions for Model Implementation. The development outcomes of English language learning management for communication were at the highest level overall. The evaluation results of the model indicated its suitability, feasibility, and usefulness. The overall satisfaction of the stakeholders with the model was also 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".