Predictive Analytics and Personalized Learning

What is the relationship between the data a university generates each day and the quality of learning its students receive? This book answers that question by combining rigorous evidence with real-world examples: how Georgia State University used an early-warning system to increase its graduation rate by more than twenty percentage points and close historic equity gaps; how Arizona State University redesigned entire courses using adaptive learning, raising the pass rate for an algebra course from 57% to 79%; and how the Open University in the United Kingdom built one of the most studied predictive analytics systems in academia to provide remote support to tens of thousands of students. Based on these case studies, the book identifies three pillars that determine whether predictive analytics truly improves learning personalization-the use of big data, satisfaction with digital tools, and the immediacy of feedback-and offers a practical framework, unprecedented in the literature on the subject, for implementing these strategies in combination with generative artificial intelligence.