Dr. Dragan Gasevic
Dr. Gasevic is a Professor and Chair in Learning Analytics and Informatics at The University of Edinburgh. Dragan is a co-founder and President of the Society for Learning Analytics Research. He is also an Adjunct Professor in the School of Interactive Arts and Technology at Simon Fraser University, Associate Adjunct Professor in the Learning and Teaching Unit at the University of South Australia, and an IBM CAS Faculty Fellow. A computer scientist by training and skill, Dragan considers himself a learning and information scientist developing computational methods that can shape next-generation learning and software technologies and advance our understanding of information-seeking, sense-making, self-regulated and social learning. Funded by granting agencies and industry in Canada, Australia, USA, and Europe, Dragan is a recipient of several best paper awards at the major international conferences in learning and software technology. The award-winning work of his team on the LOCO-Analytics software is considered one of the pioneering contributions in the growing area of learning analytics. Recently, he has founded ProSolo Technologies Inc. that develops a software solution for tracking, evaluating, and recognizing competences gained through self-directed learning and social interactions. Committed to the development of international research community, Dragan had the pleasure to serve as a founding program co-chair of the International Conference on Learning Analytics & Knowledge (LAK) in 2011 and 2012. Currently serving as a founding editor of the Journal of Learning Analytics and a program co-chair of the Learning Analytics Summer Institute, Dragan is a (co-)author of numerous research papers and books and a frequent keynote speaker.
On October 12, from 12:00 pm until 1:00 pm (Central Time), in the LINK Lab (246 Nedderman Hall)
Dr. Gasevic delivered the following public presentation:
Title: Understanding the Dynamic Nature of Learning is necessary to support Personalized Learning
Personalized learning is one of the main ideals that many educational institutions strive to provide for their students. Learning analytics with its promise to help understand and optimize learning and the environments in which learning happens has eagerly been received in this context. Existing research in learning analytics has dedicated much attention to studies that aimed at identifying factors predicting different learning outcomes based on learners’ interaction with technology. Existing research indicates that learning is a dynamic process that is driven by feedback loops. If those feedback loops are not accounted for comprehensively, opportunities for creating personalized learning experiences are limited. However, there is the dearth of research that focuses on understanding how learning unfolds over a certain period of time under different conditions. This talk will describe different factors that influence students’ feedback loops and decision making. The talk will also discuss insights gained in several case studies that looked at dynamic models of learning. The talk will conclude with an argument about the need for the development of personal learning graphs (PLeG) as a vision to for personalized learning that connects (meta)cognitive, affective, and social dimensions of learning.