Action sequence mining and behavior pattern analysis for user modeling

Tracing learner interaction with educational content has recently emerged as a centerpiece of learning analytics. Augmented by various data mining technologies, learner data has been used to predict learner success and failure, prevent dropouts, and inform university officials about student progress. While the majority of existing learning analytics approaches ignore the time aspect in the learning data, recent research indicated that not just what the learners do, but how and in which order they do it is critical to understand differences between learners, model their behavior, and predict their performance. In my talk, I will focus on the application of action sequence mining as a tool to extract temporal patterns of learning behavior and recognize cohorts of learners with divergent behavior. I will review three case studies of using sequence mining with learner data, present the obtained results, and discuss their importance for user modeling and personalization.

Bio: Peter Brusilovsky is a Professor of Information Science and Intelligent Systems and the director of Personalized Adaptive Web Systems (PAWS) lab. Peter has been working in the field of personalized learning, student and user modeling, recommender systems, and intelligent user interfaces for over 30 years. He published numerous papers and edited books on adaptive hypermedia, and the adaptive Web, and social information access. His current interests are focused on user-centered intelligent systems in the areas of adaptive learning, recommender systems, and personalized health.

Peter is a recipient of Alexander von Humboldt Fellowship, NSF CAREER Award, and Fulbright-Nokia Distinguished Chair. He served as the Editor-in-Chief of IEEE Transactions on Learning Technologies, and a program chair for several conferences including RecSys 2019. He is currently serving as the Chair of ACM SIGWEB and a board member of several journals including User Modeling and User Adapted Interaction, ACM Transactions on Social Competing and International Journal of AI in Education.