Factor Analysis of Key Success Indicators in Curriculum Quality Assurance Operation for Bachelor’s Degree in Physical Education
Bibliographic record
Abstract
The purpose of this study was to analyze the factors of key success indicators in curriculum quality assurance operation for bachelor’s degree in Physical Education. The 576 subjects were selected using cluster sampling from curriculum lecturers, staffs, and lecturers at the Academy of Physical Education Curriculum. The instrument was a related questionnaire with a 1-5 rating scale. The data were analyzed using exploratory factor analysis (EFA) with principal component analysis and orthogonal rotation by the Promax method.The results of the study revealed that there were four factors influencing key success indicators in curriculum quality assurance operation for bachelor’s degree in Physical Education, sorted by priority. The four factors are: 1) learning management and student assessment components, 2) student potential improvement components, 3) quality of lecturer components, and 4) system and mechanism of curriculum administration. Generally, the obtained factors accounted for 70.166 percent of key success indicators in curriculum quality assurance operation for bachelor’s degree in Physical Education.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".