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University of Michigan School of Information


Stephanie Teasley named co-chair for learning analytics institute

Friday, 01/22/2016

UMSI research professor Stephanie Teasley will co-chair the 2016 Learning Analytics Summer Institute (LASI 16), which will be hosted by the University of Michigan in June 2016.

LASI, which was first held at Stanford University in 2013, gives participants the opportunity to learn analytic strategies and techniques for dealing with the vast amount of data generated in education in order to better support learning and understand the impact of information technologies on the individual, institutional and cultural levels. The institute is organized by the Society for Learning Analytics Research (SoLAR).

Teasley, who is also director of U-M Digital Education Innovation’s Learning, Education & Design (LED) Lab, is program co-chair of the institute with U-M physics professor and education innovator Tim McKay.

Learning analytics is a new field that has grown quickly in recent years because of “a perfect storm of conditions,” Teasley said: increased knowledge about basic learning processes, the boom of online technology platforms and services that provide opportunities for both formal and informal learning, and the new data generated by these systems about how learning works. “When you couple this with the increasing societal pressures to make learning accessible to all and relevant to life in the information age, we need scholarly research to ensure that advances in learning technologies and practices benefit society as a whole,” she said. "Learning analytics looks for meaningful patterns in data that can be turned into action."

Up to 150 applicants from diverse disciplines, skill sets and seniority levels will be selected beginning in February to participate in the three-day-long institute, where they will have the opportunity to attend keynote talks and panel discussions and gain hands-on experience with tools and methods for analyzing data. Teasley and McKay will utilize a synthetic (to protect identities) dataset based on real U-M data in the institute workshops, enabling participants to learn analytic strategies and techniques while considering the results in context.

- UMSI News Service