Predictive Modeling for High Impact Active Learning Classrooms

Abstract

Over the past several decades, a large body of research has shown that undergraduate science students learn more and more equitably in active learning classrooms; however, the term "active learning" lacks definition and little research has examined which types and combinations of active learning strategies are most effective. In this study, we use a dataset representing over 10,000 students and 24 institutions to create a predictive model that maps classroom time spent on different activities to student conceptual learning. We find that four variables -- classroom time spent on lecture, group worksheets, clicker questions, and student questions -- are sufficient to reliably predict student learning, as measured by concept inventory scores. We identify one type of class that consistently demonstrates exceptional student learning gains (effect sizes greater than 2): those that spend 10-20% of class time on group worksheets, 20-40% of class time on group clicker questions, and average two or more student questions per hour of class time. We also find that classes which do not utilize group worksheets consistently have learning outcomes comparable to fully lecture classes. These results provide testable recommendations for future controlled studies to investigate effective active learning implementation in undergraduate physics courses.