A hybrid data envelopment analysis approach to analyse college graduation rate at higher education institutions
Bibliographic record
Abstract
College graduation rates have become a primary focus in measuring institutional performance and accountability in higher education. In 2009, President Obama set a goal for the United States to have the highest proportion of college graduates in the world by 2020. With the heightened focus on transparency and accountability in higher education today, university administrators are developing internal strategies to improve graduation rates. In fact, it is not only significantly important to institutions, but also to individuals and to the nation as a whole to increase college graduation rates. In this paper, a hybrid data envelopment analysis (DEA) approach is implemented for the very same purpose by combining with the cross industry standard process for data mining (CRISP-DM) methodology. The approach is illustrated by a case study at a U.S.-based four-year public university. We identify the most important predictors of graduation which help improve graduation rates by the CRISP-DM method. It shows that Fall term grade point average (GPA), Housing status, High school and Spring term GPA were the four highest determinative factors while monetary variables and the ethnic background of the student were revealed to be the least important ones. The results also indicated that students living on campus were more likely to complete within six years. For the detailed improvement strategies for increasing college graduation rate, we use the hybrid DEA methodology (an input-oriented bounded-and-discrete-data DEA model and context-dependent DEA) to evaluate the performance of college undergraduate students. These analyses provide potentially useful information and policy support for university administrators.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".