Tuesday 12pm, 20 October 2015
Personalizing online education using the MOOClet Framework: Designing technology to enable collaborative experimentation by instructors and researchers in statistical machine learning, psychology, and education
Joseph Jay Williams
Research Fellow - Harvard VPAL Research
The formalism of a MOOClet provides a Framework for how instructors and researchers in psychology, education, and statistical machine learning can conceptualize, design and use technology for digital education. MOOClets align instructional improvements in digital educational resources (like lessons, exercises, questions) with the advancement of scientific research on learning technologies. The Framework defines MOOClets as modular components of online courses that can be modified to create different versions, which in turn can be iteratively and adaptively improved and personalized to users through experimental comparisons that identify what is better, and for whom.
We present examples of interdisciplinary experiments that improve learning from personalized worked examples and engagement in response to personalized emails in MOOCs. These studies also show how MOOClets can be automatically improved and personalized via an API using a broad class of machine learning and AI algorithms for reinforcement learning agents (such as multi-armed bandits and Markov Decision Processes). A recording of this talk will be streamed live and be available afterwards at the url tiny.cc/williamstalk.
Joseph Jay Williams investigates intelligent adaptive technologies for personalized learning. His research bridges human-computer interaction and computational cognitive science: drawing on statistical machine learning, psychology, and education. He designs and uses real world online lessons to enable randomized experiments to discover how to personalize learning, and embeds machine learning/AI algorithms for real-time causal discovery and personalization.
He is a Research Fellow at Harvard VPAL Research, the office for online learning research and development. He is also a member of the Intelligent Interactive Systems Group in Harvard Computer Science, and leading the advisory board for an NSF Cyberinfrastructure grant to Neil Heffernan at WPI to enable psychology, education, and machine learning researchers to embed experiments in the ASSISTments online mathematics platform. He completed a postdoc at Stanford University in the Graduate School of Education working with the Office of the Vice Provost for Online Learning and Candace Thille's Open Learning Initiative. He received his PhD in 2013 doing Computational Cognitive Science in UC Berkeley's Psychology Department. As part of the Concepts and Cognition Lab he investigated why prompting people to explain "why?" helps reasoning, and in the Computational Cognitive Science Lab developed models of reasoning, decision-making and learning using Bayesian statistics and machine learning. He is originally from Trinidad and Tobago. More information about his research and papers is at www.josephjaywilliams.com/research-overview.