Evaluation of a Project-Based Learning Model for Enhancing Computational Science Teaching Skills of Master Teachers in Prachuap Khiri Khan Province
Bibliographic record
Abstract
This research aimed (1) to evaluate a project-based learning model for enhancing computational science teaching skills of master teachers in Prachuap Khiri Khan Province, and (2) to assess the validity of a structured interview form designed to evaluate these teachers’ computational science teaching skills. The target group consisted of five experts selected through purposive sampling, including specialists in instructional management, educational measurement and evaluation, and computational science. The research instruments comprised (a) a conceptual framework of the project-based learning model for promoting computational science teaching skills among master teachers, and (b) a structured interview form for assessing computational science teaching skills. Data were analyzed using percentage, mean, and standard deviation. The results revealed that: The project-based learning model for enhancing computational science teaching skills of master teachers was rated as highly appropriate (M = 4.84, S.D. = 0.29). All items in the structured interview form obtained an Index of Item-Objective Congruence (IOC) value of 1.00, which exceeded the minimum criterion of 0.50. This indicates that the developed interview form was consistent with the research objectives and suitable for data collection.
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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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".