Creating the Institutional Capacity to Leverage Learning Analytics in Higher Education
Prof. Stephanie Teasley
Dr. Teasley is a Research Professor in the School of Information and the Director of the Learning Education & Design Lab (LED Lab) at the University of Michigan. She received her PhD in cognitive psychology from the University of Pittsburgh in 1992. Throughout her career, her work has focused on issues of collaboration and learning, looking specifically at how sociotechnical systems can be used to support effective collaborative processes and successful learning outcomes. She is the co-editor of the volume, Perspectives on Socially Shared Cognition, and co-author of several highly cited book chapters on collaborative learning. Her recent work has focused on assembling and utilizing institutionally-held student data to design and evaluate new ways to support student success. She has been a Program Chair for the Learning Analytics and Knowledge conference (LAK 14) and co-chairs the Learning Analytics Summer Institute (LASI 16 & 17). She became the President of the Society for Learning Analytics Research (SoLAR) in 2017.
Home Page: https://ledlab.si.umich.edu/
Abstract: The research community for Learning Analytics is growing rapidly promising new insights into learning and resulting innovations in pedagogy. For this promise to be realized, however, we need the capacity to leverage educational data for scholarly research and apply research results at the kind of scale that truly changes how we teach and learn. In this talk I will present how the University of Michigan is engaged in learning analytics as an institutional initiative aimed at using the data produced by digitally-mediated education to better understand and improve student outcomes. I will provide the history of this initiative and outline some of the key drivers that has created the capacity for us to explore new tools and practices that may change significantly how we educate our students.
Envisioning and Grounding New Educational Designs in Data Driven Approaches
Prof. Gerhard Fischer
Gerhard Fischer is a Professor Adjunct and Professor Emeritus of Computer Science, a Fellow of the Institute of Cognitive Science, and the Director of the Center for Lifelong Learning and Design (L3D) at the University of Colorado at Boulder. He is a member of the Computer Human Interaction Academy (CHI; 2007), a Fellow of the Association for Computing Machinery (ACM; 2009), and a recipient of the RIGO Award of ACM-SIGDOC (2012). In 2015, he was awarded an honorary doctorate from the University of Gothenburg, Sweden. His research has focused on new conceptual frameworks and new media for learning, working, and collaborating, human-centered computing, and design. His recent work is centered on quality of life in the digital age, social creativity, meta-design, cultures of participation, design trade-offs, and rich landscapes for learning (including MOOCs).
Publications at: http://l3d.cs.colorado.edu/~gerhard/papers.html
Home page: http://l3d.cs.colorado.edu/~gerhard/
Abstract: Data driven approaches have enhanced learning in many different ways particularly in technological environments in which the interactions of learners can be easily tracked, analyzed, predicted, and visualized. The “right kind” (not all of them) of data are of critical importance to understand “how things are”. The presentation will focus on theories, methods, and drawbacks based on specific examples that I consider a challenge of equal (if not more) importance: how can data driven approaches provide insights and foundations for envisioning new educational designs to explore “how things could or should be?”