Designing Learning Analytics for Humans with Humans, Prof. Alyssa Wise

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Title: Designing Learning Analytics for Humans with Humans

Learning analytics (LA) is a technology for enabling better decision-making by teachers, students, and other educational stakeholders by providing them with timely and actionable information about learning-in-process on an ongoing basis. To be effective LA tools must thus not only be technically robust but also designed to support use by real people. One powerful strategy for achieving this goal is to involve those who will (hopefully!) use the learning analytics in their design. This can be done by observing existing (pre-analytic) teaching and learning practices, gathering information from intended users, or directly engaging them in participatory design. Such attention to people and context contributes to the development of Human-Centered Learning Analytics (see the recent special section in JLA 6(2)).

In this webinar, I'll present a diverse set of examples of the ways that NYU's Learning Analytics Research Network (NYU-LEARN) is including educators and students in the process of building and implementing learning analytics. We'll look at examples of how to: involve students in the creation and revision of learning analytics solutions for their own use; work with instructors to align analytically available metrics with valued course pedagogy; and partner with an educational team to design and implement interventions based on at-risk students predictions. Webinar attendees will gain a sense of both the conceptual issues and practical concerns involved in designing learning analytics for humans with humans.

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Dr. Alyssa Wise is Associate Professor of Learning Sciences and Educational Technology at New York University and the Director of 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 is situated at the intersection of learning and educational data sciences, focusing on the design of learning analytics systems that are theoretically grounded, computationally robust, and pedagogically useful for informing teaching and learning. Dr. Wise serves as Co-Editor-in-Chief of the Journal of Learning Analytics is a Co-Editor of the Handbook of Learning Analytics.

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