Catalyzing a Culture of Care and Innovation Through Prescriptive and Impact Analytics To Create Full-Cycle Learning

Abstract

Student success, both during and after college, is central to the mission of higher education. Within the higher-education and, more specifically, the student-success context, the core raison d'être of machine learning (ML) is to help institutions achieve their social mission in an efficient and effective manner. While there should be synergy among people, processes, and ML, this synergy is not often realized because ML algorithms do not yet connect the dots on fully understanding and strategically fostering student success. Transitioning from risk to impact prediction is a catalyst for institutional transformation, which can lead to continuous learning and student-success process innovation. This paper explores how ML can complement and facilitate organizational transformation in promoting a culture of care and innovation through virtuous full-cycle learning.