slam:About
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About SLAM
Beginning in Fall 2011, the Symposium on Learning Analytics at Michigan (SLAM) is designed to bring together faculty, students, and staff who are focused on understanding the academic mission of the university through the investigation of analytic data with experts in data mining, management, and visualization. We hope to spark conversation and collaborations which will connect central questions of student success with the next generation tools able to address them.
What is Learning Analytics?
Also known as academic analytics, learning analytics blends large data sets, statistical techniques, and predictive modeling and applies that combination to academic data including student admission records, academic history, and use of campus information technologies. While business applications (a.k.a. “business intelligence”) of these techniques focus on recommendations for customers’ future purchases, higher education applications have been used to support decision-making related to student admissions and housing, and more recently, to develop predictive models aimed at increasing student retention and identifying students at risk for academic failure.
When and Where Does SLAM Meet?
The symposium meets every-other Wednesday from 12pm-1:30pm in the Creative Commons Atelier (North Quad 2435) beginning September 14th. Please see the schedule for a list of speakers and topics.
How Do I Join the Conversation?
There is a M-Community group email list that you can join by clicking here. We are also developing a wiki that contains information about people, projects, tools, and resources related to learning analytics. Please feel free to add your personal and project details, as appropriate.
The Symposium on Learning Analytics at Michigan is funded by the Rackham Graduate School and Arthur F. Thurnau Professor Timothy McKay.
