Predictive Analytics Machinery for STEM Student Success Studies

Abstract

Statistical predictive models play an important role in learning analytics. In this work, we seek to harness the power of predictive modeling methodology for the development of an analytics framework in STEM student success efficacy studies. We develop novel predictive analytics tools to provide stakeholders automated and timely information to assess student performance toward a student success outcome, and to inform pedagogical decisions or intervention strategies. In particular, we take advantage of the random forest machine learning algorithm, proposing a number of innovations to identify key input thresholds, quantify the impact of inputs on student success, evaluate student success at benchmarks in a program of study, and obtain a student success score. The proposed machinery can also tailor information for advisers to identify the risk levels of individual students in efforts to enhance STEM persistence and STEM graduation success. We additionally present our predictive analytics pipeline, motivated by and illustrated in a particular STEM student success study at San Diego State University. We highlight the process of designing, implementing, validating, and deploying analytical tools or dashboards, and emphasize the advantage of leveraging the utilities of both statistical analyses and business intelligence tools in order to maximize functionality and computational capacity.