Search
Program Calendar
Browse By Day
Browse By Time
Browse By Person
Browse By Room
Browse By Unit
Browse By Session Type
Search Tips
Annual Meeting Registraion, Housing and Travel
Personal Schedule
Sign In
Session Type: Symposium
The relatively new field of Learning Analytics holds great promise for education, but that promise may be as challenging to fulfill as it is ambitious. For example, it has proven surprisingly difficult to show that greater access to data can significantly improve learning on a large scale. To address that evidence gap, this panel discussion session will bring together eminent researchers and others who work in learning science, assessment design and validation, data mining, and policy making. Panelists will discuss with the audience how learning analytics can be organized and conducted to represent a key down-payment on improving equity for learning and career success for all students, and will present the evidence that supports their recommendations.
Can Learning Analytics Improve Learning? - Bror Valdemar Haug Saxberg, Chan Zuckerberg Initative; Ryan Shaun Baker, University of Pennsylvania; David Michael Niemi, Kaplan; Richard E. Clark, University of Southern California; Mary Ann Wolf, The Friday Institute (NC State University); Roy D. Pea, Stanford University
Educational Data Mining Proof Cases - Ryan Shaun Baker, University of Pennsylvania
Analytics to Improve Persistence, Motivation, and Learning - David Michael Niemi, Kaplan; Richard E. Clark, University of Southern California
Policy Enablers and Barriers to Maximizing Learning Analytics - Mary Ann Wolf, The Friday Institute (NC State University)
Unifying the Complex Field of Learning Analytics - Roy D. Pea, Stanford University