A Synthesis of the Results of the Third Round of External Quality Assessment in Higher Education: Rajabhat University Cluster in Thailand
Bibliographic record
Abstract
The objectives of this research were to synthesize the results of assessment and analyze strengths, weaknesses that should be developed from the results of the third round of quality assessment in the higher education level of Rajabhat University cluster. The target group of the research consisted of 40 reports of the third round of external quality assessment B.E. 2554-2558 (2011-2015) in higher education of the Rajabhat University cluster. Secondary data were obtained from the external quality assessment reports. The data were analyzed using frequency, percentage and the mean. The results were as follows: 1) The synthesis of the results of the third round of external quality assessment of Rajabhat Universities showed that all of the 40 universities had been accredited, with the overall quality in the good level (Mean = 4.22). Three universities (7.50%) were in the very good level and 37 universities (92.50%) were in the good level. 2) The strengths of Rajabhat universities were on the Aspects of Academic Services to Society and Preservation of Art and Culture. The weaknesses that should be developed were on the Aspect of Research and Creative Work.
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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 | 0.073 | 0.133 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.026 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 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".