A hybrid data envelopment analysis approach to analyse college graduation rate at higher education institutions. (English) Zbl 1509.90089
Summary: 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.
MSC:
| 90B50 | Management decision making, including multiple objectives |
Keywords:
college graduation; predictive modelling; data envelopment analysis (DEA); efficiency in education; decision analytics; data miningSoftware:
DEAFrontierReferences:
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