«How Learning Analytics Can Inform E-Learning in the New Normal»




Alyssa Wise


Learning Analytics leverages new forms of data and data science methods to help us better understand and improve how we engage in teaching and learning. As we enter the new normal landscape of remote, blended and hybrid instruction, the attraction of data-informed decision-making to inform e-learning is high. But what kinds of things can (and can't) easily available data tell us about what is going on online? What insights can investment in more sophisticated analytics produce? Equally importantly: How can we (instructors, students, advisors, administrators) use this kind of information to make better choices in how we go about teaching and learning? What considerations need to be taken into account at an institutional level in order for such efforts to be successful? Drawing on the rich ecosystem of learning analytics we have built over the last five years at NYU, I will address this collection of questions as a starting point for teachers, learners, leaders and all members of the university community to consider the ways that analytics can be of value to them as they engage with e-learning in the new normal.



Author's Biography

Alyssa Wise



Dr. Alyssa Wise is Associate Professor of Learning Sciences and Educational Technology at New York University and the Director of NYU-LEARN, NYU's pioneering university-wide Learning Analytics Research Network. She holds a Ph.D. in Learning Sciences and an M.S. in Instructional Systems Technology from Indiana University, and a B.S. in Chemistry from Yale University. Dr. Wise’s research focuses on the design of learning analytics systems that are theoretically grounded, computationally robust, and pedagogically useful for informing teaching and learning. Her most recent work has focused on analytics of collaboration, reflection, and math learning and how people take up analytics as part of their educational practices. Dr. Wise serves as Co-Editor-in-Chief of the Journal of Learning Analytics, is a Co-Editor of the Handbook of Learning Analytics, and has produced many high-impact publications on the identification and application of useful traces of learning to inform educational decision-making.

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