The number of students attending community colleges that take advantage of transfer pathways to universities continues to rise. Therefore, there is a need to engage in academic research on these students and their attrition in order to identify areas to improve retention. Community colleges have a very diverse population and provide entry into science, technology, engineering, and math (STEM) programs, regardless of student high school preparedness. It is essential for these students to successfully transfer to universities and finish their STEM degrees to meet the global workforce demands. This research develops a predictive model for community college students for degree completion using the Mahalanobis Taguchi System and regression. Data collected from a Midwest community college over a five-year period in three specific associate degree programs will be used for the study. The study identified 92 students that completed a STEM degree within three years, while 730 students were not able to complete the degree within that period or at all. The research illuminates specific areas of concern related to community college students and better informs transfer institutions about this important sector of transfer students. Especially revealing is the important predictive factors traditionally found in research for STEM retention had very low correlation for this set of community college students. This research reinforces the need to investigate community college students more closely and through a different lens.

Meeting Name

2018 ASEE Annual Conference and Exposition (2018: Jun. 24-27, Salt Lake City, UT)


Engineering Management and Systems Engineering

Keywords and Phrases

Community college; Diversity; Education; Mahalanobis taguchi system; Predictive analytics

International Standard Serial Number (ISSN)


Document Type

Article - Conference proceedings

Document Version

Final Version

File Type





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