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Session Type: Symposium
This symposium will introduce new ways of understanding learning by combining multiple sources of data with microgenetic data coming digital learning environments. The presenters—representing leaders in the fields of learning analytics, educational data mining, and the learning sciences—will each describe the ways in which they use data to understand learning, learning behaviors, and the ways in which learning behaviors change over time. The panel of speakers will highlight new advances in how to use data from digital environments in approachable ways so as to promote the dual goals of advancing research on learning and building a community to facilitate the diffusion of best practices.
What Is the Future of Data, Technology, and Schools? Some Tensions - Richard R. Halverson, University of Wisconsin - Madison
Toward Integrating Data Mining and Knowledge Engineering for Better Student Models - Ryan Baker, Teachers College, Columbia University; Luc Paquette; Michael A. Sao Pedro, Worcester Polytechnic Institute & Apprendis LLC; Janice D. Gobert, Worcester Polytechnic Institute
Facial Features for Automatic Detection of Student Affect With Newton's Playground in the Wild - Sidney K. D'Mello, University of Notre Dame; Nigel Bosch; Ryan Baker, Teachers College, Columbia University; Jaclyn Ocumpaugh, Worcester Polytechnic Institute; Valerie J. Shute, Florida State University; Matthew Ventura, Florida State University; Lubin Wang, Florida State University; Weinan Zhao, Florida State University
Using Action Sequences as Evidence of Proficiency - Kristen E. Dicerbo, Pearson
Measuring the Learning Quality of Open Educational Resources in Mathematics - Zachary Pardos