Research on Health Status Evaluation of Higher Education Based on Factor Analysis and Projection Pursuit Evaluation Model
Bibliographic record
Abstract
Higher education system is an important factor for a country to provide its citizens with further education besides primary and secondary education, each country has different higher education systems, and each country has its own advantages and disadvantages. In order to cultivate higher quality talents, countries should begin to pay attention to the reform of higher education system in order to establish a healthier and sustainable higher education system. Firstly, we set up a national health evaluation model of higher education. The first step is to select 40 countries' data about higher education by consulting relevant literature and OECD database, and use factor analysis to select and process evaluation indicators, Finally, four first-level indicators, such as teachers' strength and learning environment, students' access to education, education financial investment, education level and achievements, and 12 second-level indicators, such as the ratio of institutional educators and international students, the proportion of students' expenditure on education to GDP, and the total expenditure of government educational institutions, are determined. In the second step, we first normalize the index data, and then nest the projection pursuit evaluation model with multiple indexes. We use the evaluation value of every three secondary indicators to represent the corresponding primary indicators, and then calculate the evaluation value of the primary indicators as the final result, so we can get the weight formula of all indicators. The analysis results show that the health status of higher education in Korea is poor, and the evaluation values of most indicators are lower than the average level, so we chose Korea as the follow-up research object and put forward an achievable and reasonable vision for its higher education system.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".