Development of a Training Curriculum on Social Engineer Competency Based on the 5C Model to Enhance Problem-Solving and Adaptability Skills for Students at Surat Thani Rajabhat University and Residents of Khun Thale Subdistrict, Mueang Surat Thani, Thailand
Bibliographic record
Abstract
The purposes of the current study were to examine the 5C social engineering model-based training program on Surat Thani Rajabhat University students and Khun Thale Subdistrict residents' problem solving and adaptability and to examine the participants’ satisfaction learning with the 5C social engineering model-based training program. A training program was designed and implemented to enhance these skills among 150 students from Surat Thani Rajabhat University and local residents of Khun Thale Subdistrict, Mueang District, Surat Thani Province. The program focused on fostering collaborative learning, interdisciplinary engagement, and innovative problem-solving within a community-based context. The results demonstrated that participants significantly improved their problem-solving and adaptability skills, reinforcing the effectiveness of the 5C Model in cultivating social engineering competencies. The study also highlights the model’s potential for area-based community development, where educational institutions play an active role in addressing local challenges. These findings suggest that the 5C Model can inform university policies aimed at regional development and sustainable growth through socially engaged learning approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".