Evaluation on Higher Education Using Data Envelopment Analysis
Bibliographic record
Abstract
The goal of higher education is to provide students an equal opportunity to access their education for success. With significant competition within the peer group, potential students look for quality, flexibility, and affordability in the educational environment. In addition, the relationship between students and the institution involves a concentrated and more specific set of expectations. In order to improve students’ academic performance and fulfill individual needs, universities aim to enhance the quality of students’ learning environment and academic achievements. The higher education system relies on efficient operation and strategic planning to fulfill students’ needs through an internal emphasis on institutional performance improvement. A study on measuring the performance of higher education is presented. The research was focused on four-year and above, public and not-for-profit private universities in the southern region (AL, AR, KY, LA, MS, OK, TN, and TX) of the United States. The data includes 270 universities which were obtained from the Institute of Education Sciences, U.S. Department of Education. This study applied the Data Envelopment Analysis (DEA) approach; the purpose is to use a linear programming model to demonstrate a novel benchmarking process of higher education institutional performance and determine an overall benchmark for institutions within each classified group. From the results, suggestions are provided for the general guidance of planners and decision makers in the higher education system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.043 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".