The Development of Young Researchers by the Professional Learning Community of the Thailand National Sports University
Bibliographic record
Abstract
This research and development project aims to develop young researchers at Thailand National Sports University using the Professional Learning Community (PLC) framework. The research was conducted in four phases: studying the research problem, and developing a 5-step approach to young researchers through PLC, experimentation, and lessons learned success factors. The project had 36 participants. The results revealed that factors promoting research scored an average of 3.45, indicating a high level. Similarly, knowledge, skills, and research experience averaged 3.46, also reflecting a high level. The 5-step PLC process utilized for fostering young researchers includes establishing shared values and norms, collaborative practice toward a common goal, professional community cooperation, exposure to guidance, and reflective dialogue. As a result, six developmental approaches for young researchers were identified: self-development in knowledge, skills, and experience; preparation of research projects; writing research project proposals; implementing research projects; time management; and creating a conducive research atmosphere on campus. Implementing these approaches led to the formulation of 21 research proposals. Evaluation of these proposals yielded an average score of 3.51, meeting the criteria with a 100% success rate. Noteworthy aspects include openness, utilization of external expert networks, adjustment of attitudes towards research significance, and administrative support for research. In summary, this study underscores the significance of the 5-step PLC process as a pivotal approach in nurturing the next generation of researchers. Thailand National Sports University and other institutions can adopt this process to foster a culture of research and development among faculty and students.
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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 | 0.036 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".