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SAM ADVANCED MANAGEMENT JOURNAL
Big Data Analytics Modeling To Address The Retention Challenges And Dropout Rates Facing Higher Education
Parthasarati Dileepan, Mohammad Ahmadi, and Marilyn M. Helms
DOI:
Citation: Dileepan, P., Ahmadi, M. & Helms, M.M. (2023). Big data analytics modeling to address the retention challenges and dropout rates facing higher education. SAM Advanced Management Journal, 88(3),56-74.
Abstract
Improving student retention, reducing dropout rates, and boosting graduation rates are among the top priorities of higher education institutions. A driving force for this priority is the reality that state funding for higher education is tied to these measures. Institutions of higher education have invested scarce resources to address this challenge. For example, many, if not all, institutions have established support services dedicated to helping students succeed academically. These support services help students throughout their entire college experience starting from freshman year to job search and placement in their senior year. While these are important factors that help students, they do not proactively seek or identify at-risk students. It is imperative to determine the main factors that cause students to drop out. An abundance of research suggests student engagement in their first year of college is an important factor but suggest additional research should go beyond how to keep students engaged. Other studies have introduced factors or treatments that might affect the behavior of students and decrease attrition. Yet, there are other studies that agree student retention is determined by creativity, emotional intelligence, and learner self-sufficiency. the focus of this study is to understand the factors that contribute to students dropping out and develop a model that can predict students at risk of dropping out so that intervention strategies can be developed to mitigate this risk. To explore the retention issue, this research uses a data analytics approach that evaluates demographics, student performances, and other key variables to identify students at risk of dropping out so timely and individualized intervention can be initiated to avoid a negative outcome for both the student and the institution. Data spanning ten years from a major SACSCOC-accredited metropolitan university, part of a state-wide system, and covering 209,463 student course registrations and 40,581courses was used in this research. A Logistic Regression model was used to predict and prioritize at risk students. The model predicted the at-risk status of students with 84% accuracy rate and students who were identified as at-risk students were predicted at 73%. Further, Akaike Information Criterion was used to identify the factors most useful for predicting the risk of a student dropping, and included major, overall GPA, number of hours dropped, earned credit hours, and if they were a first-generation college student. Study limitations and areas for future research are also included.
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