Strengthening Teacher’s Learning Management for Self-Reliance of Students in Thai Secondary School
Bibliographic record
Abstract
This research aimed to 1) study the present conditions, problems, and needs of teachers development in learning management in self-reliance for students in secondary schools, 2) develop a model for teacher development in learning management in self-reliance for students in secondary schools, and 3) evaluate the results of usage from teachers in learning management in self-reliance for students in secondary schools. Research methodology was based on research and development (R&D) approach by the application of Participatory Action Research (PAR). There were seven processes ans stages involved as follows 1) study of the best practice, 2) participatory workshops to create awareness of the participants, 3) solutions and resources to improve teaching and learning of teachers, 4) creating alternative solutions and develop curriculum, 5) developing of analytical thinking skills and synthesis of curriculum in leaning management, 6) linking and transfer of knowledge, skills, attitudes of learning into practices, and 7) evaluation and reflection on the performance of teachers in learning to self-reliance of the student. The results showed that the model for teachers in learning management for self-reliance of students in secondary schools consisted of two main elements as follows 1) the process of participatory learning activities in seven steps, 2) the operation of participatory activities in fourteen sub-activities. The evaluation shown that the level of possibility, appropriateness, and usefulness were in 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".