Facilitated by Dr. George Rehrey, University of Indiana Bloomington
Today institutions of higher learning are constantly accumulating vast amounts of data through a network of widely diverse, yet loosely coordinated operational systems (e.g., course learning management systems, student information systems, student activities systems, card swipes at academic support centers, etc.). At the same time, new means for making sense of this “Big Data”, including predictive models and new visualization tools, have rapidly emerged as new types of evidence that can help us better understand teaching, learning, and student success. In this presentation I will discuss how the use of Student Information Systems (SIS) data can provide a new viewpoint for thinking about and measuring the impact of course transformation initiatives, and how that data can also be leveraged to change departmental cultures in higher education.
George Rehrey is the founding director of Indiana University’s Center for Learning Analytics and Student Success (CLASS). From 2007-18, he was the director of Scholarship of Teaching and Learning Program at Indiana University Bloomington.George serves on the Steering Committee for the Bay View Alliance, an international network of nine research universities in the US and Canada that are exploring strategies to support and sustain the widespread adoption of instructional methods that lead to better student learning and success within STEM. He is also an advisory board member for Taking Evidenced-based Action (T.E.A.), a national coalition of campuses using student learning analytics to improve student engagement and success.
George’s current research focuses on faculty use of learning analytics, big data and predictive modeling as a way to advance student success at the course, program and institutional levels within higher education. This research also includes understanding the influence social and economic reward systems may have upon academic development programs.