Professional Competence Development of Social Studies Teacher in Thailand Education Sandbox
Bibliographic record
Abstract
This research article deals with two objectives. The first objective was to develop the professional competence of social studies teacher in the education sandbox. The second objective was to propose social studies classroom-based action learning management innovation to develop professional competence of social studies teacher in the education sandbox and to assess satisfaction levels of students in developing professional competence of social studies teacher in the education sandbox. The conducted research in this article was classroom-based action research whereby the assessment form of area-based competence perception of educational students in Chiang Mai education sandbox, Faculty of Education, Chiang Mai University (pre- and post-assessment). The sample group used in the research consisted of senior educational students in the field of social studies of the second semester of the academic year 2020 accounting for 30 people. For the analysis, descriptive statistics were used. Results of statistical data analysis were shown as mean and standard deviation. From the research results, it was found that the level of perceived competence of becoming a professional teacher in Thailand education sandbox consisted of 3 aspects. 1) For the aspect of self-efficacy regarding knowledge and understanding in becoming a professional teacher, the average summation of the 4 competencies was as follows: the perception was at a high level. 2) For the aspect of skills and capacity in becoming a professional teacher in the Chiang Mai education sandbox, the average summation of 4 competencies was as follows: the perception was at a high level. 3) For the aspect of attitudes on becoming a professional teacher in the Chiang Mai education sandbox, the average summation of 4 competencies was as follows: the perception was at a high 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".