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Andrew Lan - Machine Learning Methods for Personalized Learning

DS Seminar
Monday, April 2, 2018 - 4:00pm
Computer Science Building, Room 150/151

A reception for attendees will be held at 3:30 p.m. in CS 150

Faculty Host: Andrew McCallum

 

Abstract: Personalized learning, i.e., recommending personalized remediation or enrichment activities to each learner based on their individual background, interests, learning goal, and learning progress, has the potential to revamp the outdated "one-size-fits-all" approach in education. The key to achieving scalable personalized learning is to leverage recent developments in machine learning (ML) and big data systems to collect and analyze learner and content data and automatically generate actions for personalization. Developing ML methods for personalized learning comes with significant challenges since (i) it is difficult to design interpretable and robust learner models due to the complex nature of human behavior and (ii) the outcomes of education (e.g., employment) are of paramount importance to the learners, which places a premium requirement on the safety of personalization.

In this talk, Andrew will propose novel ML methods that improve the interpretability and safety of personalized learning. First, he will introduce a series of learner-response models that offer superior interpretability on the knowledge structure of assessment questions. Then, he will propose a linearization method that provides exact, nonasymptotic expressions of the error in learner and content parameter estimates, which is critical to the design of fail-safe personalization algorithms.

 

Bio:
Andrew S. Lan is a postdoctoral research associate in the EDGE lab at the Department of Electrical Engineering at Princeton University. His research interests are in the development of ML methods for educational applications including learning and content analytics, personalized learning action selection, automated grading and feedback, and social learning. His work has resulted in over 20 publications in top conferences and journals in machine learning and educational data mining. His algorithms for personalized learning are integrated into OpenStax Tutor, the commercial-grade personalized learning platform of OpenStax; In the current academic year, nearly 1.5 million U.S. college students are using OpenStax's collection of 29 free, online textbooks. He has also organized a series of workshops on machine learning for education; see http://ml4ed.cc for details. He received his B.S. degree in physics and mathematics from the Hong Kong University of Science and Technology, and his M.S. and Ph.D. degrees in electrical engineering from Rice University in 2014 and 2016, respectively.