ECE Seminar: Machine Learning for Human Learning, Dr. Andrew Lan, Princetion University


11:45 am-1:00 pm
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234 Larsen Hall
UF Campus
Gainesville, Florida


Dr. Andrew Lan
Machine Learning for Human Learning

Human learning, both in the standard classroom setting and in other settings, e.g., online learning, is crucial to advancing human intelligence. Personalized learning, i.e., recommending personalized remediation or enrichment activities to each learner based on their individual background, interests, and learning progress, has the potential to significantly improve human learning.
In this talk, I will present a series of machine learning (ML) methods towards the goal of delivering personalized learning experiences at large scale. First, I will discuss a series of novel interpretable learner-response models that characterize the complex relationship between assessment questions and underlying concepts; these models give us an intuitive understanding of the knowledge state of each learner. Then, I will discuss a novel linear estimator that provides an exact and nonasymptotic analysis of the estimation error in learner and content parameters; this error analysis is more accurate than common asymptotic analyses for realistic problem sizes, thus improving the reliability of personalization.

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 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,


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