Lecture Title: Perpetually Enhancing Educational Technology Through Dynamic, Personalized, Collaborative Experimentation
Speaker: Joseph Williams, National University of Singapore's School of Computing
Abstract: How can we use data from real-world users to rapidly enhance and personalize educational technologies? I show how we can build self-improving systems through three applications of MOOClets, a conceptual framework implemented in technology that leverages randomized A/B experiments as tools for collaboration, dynamic enhancement, and adaptive personalization.
First, a novel system that enhanced learning from online problems, by crowdsourcing explanations and using reinforcement learning to automatically experiment to discover the best explanations. Second, a system which enabled three on-campus instructors at Harvard to experimentally investigate which hints and feedback messages students found helpful, enabling more ethical experimentation by dynamically presenting the best conditions to future students. Third, I show how to boost responses to an email campaign in a MOOC, by experimentally discovering how to personalize motivational messages to a user's activity level.
I'll close with future directions on tools for experimentation that can be used by computer science instructors, HCI and social-behavioral scientists, and statistical machine learning researchers.
Biography: Joseph Jay Williams is an Assistant Professor at the National University of Singapore's School of Computing, department of Information Systems & Analytics, and Translational Research Technologies Lead at the Institute for the Application of Learning Sciences and Educational Technology. He was previously a Research Fellow at Harvard's Office of the Vice Provost for Advances in Learning, and a member of the Intelligent Interactive Systems Group in Computer Science. He completed a postdoc at Stanford University in the Graduate School of Education in Summer 2014, working with the Office of the Vice Provost for Online Learning and the Open Learning Initiative. He received his PhD from UC Berkeley in Computational Cognitive Science, where he applied Bayesian statistics and machine learning to model how people learn and reason. He received his B.Sc. from University of Toronto in Cognitive Science, Artificial Intelligence and Mathematics, and is originally from Trinidad and Tobago. More information is at www.josephjaywilliams.com.
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