The Development of a Program to Enhance Learning and Classroom Management Skills for Teachers in Secondary Schools Using a School-Based Approach
Bibliographic record
Abstract
This research aimed to study the current state and needs, develop a program, and evaluate the effectiveness of a program designed to enhance learning management and classroom management skills for secondary school teachers using a school-based approach. The research was divided into three phases: 1) studying the current state and needs, 2) developing the program, and 3) evaluating the program’s effectiveness. The sample consisted of secondary school teachers, experts, and teachers from Romburipittayakhom Ratchamangkhalapisek School. Research instruments included questionnaires, interviews, and tests, and the data were analyzed using basic statistics and t-tests. The research findings revealed that: 1) the current state of learning management was at a moderate level, while classroom management was at a high level, with the desired state for both aspects being at the highest level; 2) the developed program consisted of five components: principles, objectives, content, development methods, and evaluation, with suitability and feasibility rated at the highest level; and 3) the program’s effectiveness showed that teachers had significantly higher knowledge and understanding after the development at the .05 level, exhibited high levels of behavior according to the program, and showed the highest level of satisfaction with the program.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".