Leveraging large-scale educational data to examine the freshman experience using machine learning and causal inference methods

Abstract

Institutions of higher education are constantly collecting student-centric data, be it application information, transcript records, or graduation histories. Despite this wealth of data, traditional institutions, where learning is primarily on-campus and in-person, are often limited by infrastructure and personnel in their ability to transform their data into useful information to make data-informed decisions. In this work, I employ supervised machine learning and econometric techniques to utilize data that is routinely collected at traditional institutions of higher education to improve institutional processes and better understand students. First-year, first-time students face numerous challenges as they transition to post-secondary education and, as a case study, I examine two phases of their academic careers: their enrollment decision prior to starting their college education and their first year on campus as they adjust to college life and push towards graduation. In all, this work demonstrates how academic institutions can apply data-centric techniques and contributes to long-standing education theory on post-secondary enrollment, acclimation, and persistence.