Utilizing Early Engagement and Machine Learning to Predict Student Outcomes

Abstract

Finding a solution to the problem of student retention is an often-required task across Higher Education. Most often managers and academics alike rely on intuition and experience to identify the potential risk students and factors. This paper examines the literature surrounding current methods and measures in use in Learning Analytics. We find that while tools are available, they do not focus on earliest possible identification of struggling students. Our work defines a new descriptive statistic for student attendance and applies modern machine learning tools and techniques to create a predictive model. We demonstrate how students can be identified as early as week 3 (of the Fall semester) with approximately 97% accuracy. We, furthermore, situate this result within an appropriate pedagogical context to support its use as part of amore comprehensive student support mechanism.