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PRODID:-//Memento EPFL//
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SUMMARY:Using Educational Data Science to Improve Learning in the Universi
 ty
DTSTART:20180115T111500
DTEND:20180115T120000
DTSTAMP:20260406T125521Z
UID:4ae616e256f36a5787c8469ca42c72e8724dd749871f871862ef2212
CATEGORIES:Conferences - Seminars
DESCRIPTION:Alyssa Friend Wise is Associate Professor of Learning Sciences
  and Educational Technology in the Steinhardt School of Culture\, Educatio
 n\, and Human Development. She holds a Ph.D. in the Learning Sciences and 
 M.S. in Instructional Systems Technology\, both from Indiana University\, 
 and a B. S. in Chemistry from Yale University.\n\n \n\nWise's research is
  situated at the intersection of the learning sciences and educational dat
 a science\, focusing on the design of learning analytics systems that are 
 theoretically grounded\, computationally robust\, and pedagogically useful
  for informing teaching and learning. She has published extensively on the
  identification of useful traces of learning in large data sets and the ap
 plication of these to inform educational decision-making by designers\, in
 structors and students. She has also conducted influential research on the
  design of computer-supported collaborative learning systems in both onlin
 e and physical environments and is particularly known for her pioneering w
 ork conceptualizing and researching learners' online listening behaviors. 
 Wise is a member of the Executive Committee of the Society for Learning An
 alytics Research and the Computer-Supported Collaborative Learning Committ
 ee within in the International Society of the Learning Sciences. She serve
 s on numerous journal editorial boards and in 2017 will become one of the 
 principal editors of the Journal of Learning Analytics.\n\n \n\nPreviousl
 y Wise was an Associate Professor and Coordinator of the Educational Techn
 ology & Learning Design Programs at Simon Fraser University in Canada. Her
  work has been extensively funded by the Social Sciences and Humanities Re
 search Council of Canada and widely recognized for its contributions to th
 e learning sciences and learning analytics literature.\nThere are many exc
 iting opportunities to use educational data science to understand and info
 rm teaching and learning in a university. In this presentation I will intr
 oduce LEARN\, NYU’s new Learning Analytics Research Network and describe
  three of our exciting initial projects to develop data-based practices. T
 he MOOCeology Project offers insight into online collaboration and how to 
 support it. The Calculus Lifeline Project supports early detection and sup
 port for students likely to struggle. The Instructor Dashboard Project stu
 dies how instructors are starting to use data to inform their teaching act
 ivities. Each project will be showcased in the context of institutional ch
 allenges and strategies involved in launching a university wide analytics 
 research network.
LOCATION:RLC D1 661 https://plan.epfl.ch/?room==RLC%20D1%20661
STATUS:CONFIRMED
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