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and learning progress, has the potential to revamp the outdated “one-size-fits-all”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, I will propose novel ML methods that improve the interpretability and safety
of personalized learning. First, I will introduce a series of learner-response models that
offer superior interpretability on the knowledge structure of assessment questions.
Then, I 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.
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,