Lunch will be served from 12:30-1pm, on a first-come, first served basis. The talk will begin promptly at 1pm.
Abstract: Big data and machine learning appear to be revolutionizing many fields. Is education one of them? Unlike our universe or the quantum structure of particles, how people learn is a question that seems much closer to our direct observation. So close, one might wonder why data is needed and whether self-reflection is sufficient to understand learning. Koedinger’s first goal is to convince you that self-reflection is not sufficient. His second is to provide you with examples of educational data mining and how it has provided insights into how people learn (e.g., slowly and incrementally) and fostered improvements in human learning outcomes (e.g., 2x more effective learning). Koedinger will emphasize that explanatory models of data are critical for such insights and outcomes and that disciplinary expertise, but not just data science, must be brought to bear. He will illustrate the role of disciplinary expertise in the psychology of learning and in the educational subject-matter domain, and the role of explanatory models in the form of symbolic computational models of learning that can be taught competencies like algebra, grammar, and chemistry.
Kenneth R. Koedinger is a professor of Human Computer Interaction and Psychology at Carnegie Mellon University. Dr. Koedinger has an M.S. in Computer Science, a Ph.D. in Cognitive Psychology, and experience teaching in an urban high school. His multidisciplinary background supports his research goals of understanding human learning and creating educational technologies that increase student achievement. His research has contributed new principles and techniques for the design of educational software and has produced basic cognitive science research results on the nature of student thinking and learning.