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Using Logistic Regression Model to Identify Student Characteristics to Tailor Graduation Initiatives
Chatterjee, Ayona; Marachi, Christine; Natekar, Shruti; Rai, Chinki; Yeung, Fanny – College Student Journal, 2018
Improving graduation rates is one of the biggest missions in many universities across the country and it is surely the case on the campus of this institution. The work here presents a statistical tool box to use early academic performance as a predictor for graduation with logistic regression and machine learning techniques. The methods described…
Descriptors: Regression (Statistics), Student Characteristics, Graduation, Probability
Blau, Gary; Snell, Corinne M. – College Student Journal, 2013
Professional Development Engagement (PDE) is defined as "the level of undergraduate engagement in professional development." It reflects career-related work preparation for "life after college" and is a distinct externally-focused component of student engagement (SE). The increased college retention and subsequent job placement…
Descriptors: Undergraduate Students, Professional Development, Learner Engagement, Career Readiness

Remus, William; Isa, Debra – College Student Journal, 1983
Illustrates the use of discriminant analysis to identify the background factors among accepted applicants that best predict actual attendance. An illustration for MBA programs at the University of Hawaii showed the applicant's residence and tuition status were the best predictors. (JAC)
Descriptors: College Applicants, College Attendance, Discriminant Analysis, Enrollment Influences

DeBerard, M. Scott; Spielmans, Glen I.; Julka, Deana L. – College Student Journal, 2004
The freshman year represents a stressful transition for college students. Despite a multitude of social, academic, and emotional stressors, most college students successfully cope with a complex new life role and achieve academic success. Other students are less able to successfully manage this transition and decide to leave higher education…
Descriptors: College Freshmen, Grade Point Average, Academic Achievement, Predictor Variables

Remus, William; Wong, Clara – College Student Journal, 1982
Evaluates the effectiveness of five admission models in predicting student performance in a Masters of Business Administration program. Found the regression model predicts graduate success better than the other models, but none of the models improved upon the admission officer's judgment. (Author/JAC)
Descriptors: Academic Achievement, Admission Criteria, Admissions Officers, Business Administration Education