Advancing University Teaching and Learning with Analytics: Linking Pedagogical Intent and Student Activity through Data-Based Reflection

November 17, 2014 | 12:00pm – 1:30pm
Speaker: Alyssa Wise (Simon Fraser University)

Abstract:
Learning analytics are data traces of student activity that can be used to better understand and support learning processes and outcomes. Over the last few years there have been remarkable advances in our ability to calculate and display useful information about what students are doing. Now, we face the important challenge of how to mobilize this intelligence to have a meaningful impact on university teaching and learning. To do so, we need to consider and design for the ways in which learning analytics can become a part of (and change) the activity patterns of instructors and students. Working within the scope of the university course, I describe ways to integrate learning analytics into teaching and learning processes by using data-informed reflection to probe the connections (and disconnects) between instructors’ and designers’ pedagogical intents and students’ actual activity patterns. Particular attention will be paid to roles for students in the process, and the use of different reference frames for data interpretation. To ground the discussion, work from the E-Listening Project at Simon Fraser University will be presented as an initial example of a learning analytics application developed and implemented in a university course using such an integrated approach.

Presentation Slides


Speaker Bio:
Alyssa Friend WiseAlyssa Friend Wise is an Associate Professor with the Educational Technology & Learning Design Program at Simon Fraser University in Canada. Her research examines how people interact with each other through technologies and how such interactions can contribute to learning. Recent work includes the E-Listening Project (research into how participants attends to others’ comments in online discussions), the development of Youtopia (a collaborative table-top game about sustainability issues), and the creation of a model for Learning Analytics Interventions (a pedagogical approach to help students work with data collected on their learning as part of the educational process).

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