The Effectiveness of “Knowledge Management System” in Research Mentoring Using Knowledge Engineering
Bibliographic record
Abstract
Currently, many old universities in Thailand have been facing the occurrence of lecturer massive retirement. This leads to the large amount of newly Ph. D. graduate recruitment for taking immediate responsibilities to teach and conduct research without mentoring by senior staff as well as in new universities. Therefore, this paper aims to propose the “Knowledge Management System Based Mentoring” which could be used to share and disseminate research experiences of the senior staff to enhance the abilities of newly Ph.D. graduate staff in the universities to supervise Ph.D. students to get the qualified research outputs. Knowledge engineering is employed to capture the effective mentoring practices particularly on Lateral Thinking in higher education. The Knowledge Management System had been implemented in department of Knowledge Management, The College of Arts, media and Technology, Chiang Mai University to mentor five newly Ph.D. graduate staff. The study explored the effectiveness of KMS in the case study of Ph.D. program in Knowledge Management is elicited from three senior professors (in social science, mathematics, as well as computer science and knowledge management) and modeled in CommonKADS. The Knowledge Management department is utilizing this mentoring knowledge for improving the research performance. The major output of the study is the effectiveness of “Knowledge Management System” KMS that helps to enhance abilities of newly Ph.D. graduate staff to supervise Ph.D. students productively.
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How this classification was reachedexpand
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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".