ABSTRACT
Over the past decade, the explosion of smart technology, artificial intelligence and algorithm machine learning have added a new dynamic to every layer of higher education institutions around the world. This chapter reviews the fundamental applications of predictive analytics in higher education management, the arguments for and against its implementation, develops an ethical framework to model, guide and govern these machine-driven systems and uses specific case studies to examine the positive and negative consequences of predictive analytics and how they impact on student and faculty decision-making. Most universities started to use the application of predictive analytics as a recruitment and admissions effort to target larger and more accurate pools of potential high school students. It was only later that predictive learning analytics evolved and branched off into student care, e-learning platforms and class retention efforts. After student enrolment, the creation of early warning systems at various universities piloting PLA has proven to be a resounding, overwhelming and detailed success.
