Teacher Professional Development in Research Skill of Teacher in Non-Formal Education Center, Lampang, Thailand
Bibliographic record
Abstract
The educational policy will be developing strong human competency in which teachers’ research skill was one of most competency standards. This study introduces teachers’ research skill development through the Wlodkowski’s motivational approach and coaching and to study the teachers’ attitude toward action research. This study also examines the struggles and problem of doing action research. Participants included 12 teachers who are in the non-formal education center, Lampang, Thailand. The Wlodkowski’s motivational approach and coaching were used to develop teachers’ research skill. Data was collected by using the research skill inventory (RSI) and the research attitude toward inventory (RATI) which struggle and problems of doing action research questions. Descriptive statistics were used to analyze teachers’ research skill and teachers’ attitude toward. Friedman test and Wilcoxon test were conducted to evaluate median differences among the reseach skills and created pairwise comparisons. Content analysis was used to analyze the struggles and problem of doing action research. Results revealed that in-service teachers’ research skills increased in all domains. Teachers’ research skill scores after received treatment were significantly greater than the teachers’ research skill scores before received treatment, z = -3.07, p = .002. The posttest score of teachers’ attitudes toward was significantly greater than pretest score, z = -3.08, p = .001. Teachers struggled with how to conduct research and who could help them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".