A Model of International Education Program Management: A Case Study of Sino Thai Project in Yunnan Province, China
Bibliographic record
Abstract
This study aimed to develop an innovative project management model for international education collaborations, focusing on the Sino-Thai partnership in Yunnan Province, China, which faces challenges such as cultural differences, communication barriers, and administrative discrepancies. Using a mixed-methods approach, the research integrated quantitative data from questionnaires and qualitative insights from expert focus group discussions to ensure comprehensive analysis. The proposed model comprises four key components: 1) Inputs: Institutional potentials, staff quality, educational facilities, and expenses; 2) Processes: Needs assessment, communication and collaboration, monitoring and controlling, and project review; 3) Outputs: Effective project operations, collaborative staff efforts, and project success, and 4) Feedback: A dynamic mechanism linking project outcomes to inputs for continuous improvement. The evaluation demonstrated that the model reduced communication challenges by 62% and increased stakeholder satisfaction to an average of 4.2 out of 5. Expert evaluations highlighted its practicality, with an average score of 4.5 out of 5 for suitability and utility. Unlike traditional frameworks, the model incorporates a flexible feedback loop that addresses cultural and administrative disparities, ensuring adaptability to diverse educational contexts. These findings underscore the model’s potential to enhance the efficiency and sustainability of cross-cultural educational partnerships. By bridging gaps between cultural and administrative systems, this research contributes to innovative educational practices and aligns with global efforts to foster collaborative learning environments.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".