A MACHINE LEARNING APPROACH TO PREDICT POST-SECONDARY COLLEGE ENROLLMENT AT HIGH SCHOOL LONGITUDINAL STUDY OF 2009-2016 (HSLS:09)
College access is crucial because it ensures that the students access the necessary skills to develop their careers. Higher education institutions are increasingly demonstrating interest in understanding factors that optimize student enrollment and minimize dropout rates. College access factors could enable applicants who demonstrate the most predictable attributes to register or apply in higher education institutions and understand how to provide them with a supportive learning environment. The proposed research aims to forecast post-secondary college access and recruitment for high school students by utilizing a quantitative research study. Correspondingly, the study will determine the factors that influence high school students' probability of attending a higher education institution based on their risk index. Hence, the research will focus on identifying how the choice of post-secondary educational paths could predict students' decisions for registering or applying. Other factors to be examined include the influence of teachers, counselors, parents, students', and students' aspirations on high school for students' choice of college registration. By Utilizing the High School Longitudinal Study of 2009-2016 (HSLS:09/16) dataset, several factors are expected to emerge as strongly influencing high school students' decisions on post-secondary educational application or registration decisions. This study indicates how student characteristics influence their choices to apply or register at a college or drop out once they finish high school. This research concluded that Random Forest, Logistic Regression, and Support Vector Machine classifiers executed a higher prediction rate of deciding whether students are at risk of not applying to college. Moreover, students were ranked based on their likelihood of not attending college, which would enable educators to focus on effectively concentrating their intervention resources for college access.