“Human Learning, Machine Learning, and E-Learning: Conflict or Confluence?”
By Professor Thomas C. Reeves, Professor Emeritus of Learning,
Thomas C. Reeves is a Professor Emeritus of Learning, Design, and Technology in the College of Education at The University of Georgia. He is former Fulbright Lecturer in Peru and he has been an invited speaker in the USA and more than 30 other countries. In 2003, he received the Fellowship Award from the Association for the Advancement of Computing in Education (AACE), in 2010 he was made a Fellow of the Australasian Society for Computers in Learning in Tertiary Education (ASCILITE), and in 2013 he received the Lifetime Award from the International Association for Development of the Information Society (IADIS) as well as the David H. Jonassen Excellence in Research Award from the Association for Educational Communications and Technology (AECT). His books include Interactive Learning Systems Evaluation (with John Hedberg), A Guide to Authentic E-Learning (with Jan Herrington and Ron Oliver), Conducting Educational Design Research (with Susan McKenney), and MOOCs and Open Education around the World (with Curt Bonk, Mimi Lee, and Tom Reynolds). He consults with the World Health Organization and other organizations on the development of authentic task-based e-learning, and he serves as an external evaluator for research and development projects at universities and other institutions.
Personal Webpage: http://www.evaluateitnow.com/
The technologies underlying machine learning are developing so rapidly that huge swaths of career paths previously open to university graduates are being assumed by algorithms and robots. These are not just easily-automated manual labor jobs such as filling orders at online stores, but high cognitive demand professions such as those of pharmacists and journalists. Meanwhile, politicians around the world promise to bring back high-paying manufacturing jobs and voters naively believe them, seemingly unaware or unwilling to acknowledge an emerging “techno-feudalism” wherein many, perhaps most, people will have few options other than minimum wage work or government subsistence payments in the form of a “guaranteed minimum income.” Amid this economic and social devolution, higher education institutions continue to offer degree programs in professions that may soon disappear or be drastically reduced in numbers. This keynote presentation will explore the implications of machine learning for human learners, and more specifically, for designers and providers of e-learning.