Evaluation of a Project-Based Learning Model for Enhancing Computational Science Teaching Skills of Master Teachers in Prachuap Khiri Khan Province
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it