Alexander Tuzhilin Made a Report "Recommending Remedial Learning Materials to the Students by Filling their Knowledge Gaps"

A new content-based method of providing recommendations of remedial learning materials to the students was presented. This method identifies gaps in students’ knowledge of the subject matter in the online courses that they take and provides recommendations of relevant targeted educational materials from the library of assembled learning materials to them in order to close the “gaps” in what the students have learned in the course. The proposed recommendation method is empirically validated using a randomized controlled experiment on the students from an online university. It is shown that the students not only liked the recommendations provided to them by the proposed method, but that these recommendations led to better performance results on the final exams for certain segments of the student body.This is joint work with Konstantin Bauman.

Bio: Alexander Tuzhilin is a Leonard N. Stern Professor of Business and the Chair of the Department of Information, Operations and Management Sciences at the Stern School of Business, NYU. Professor Tuzhilin’s current research interests include personalization, recommender systems and data mining. He has produced more than 100 research publications on these and other topics in various journals, books and conference proceedings. Professor Tuzhilin has served on the organizing and program committees of numerous conferences, including as the Program and as the General Chair of two IEEE International Conferences on Data Mining (ICDM), and as the Conference Chair and as the Chair of the Steering Committee of the ACM Conference on Recommender Systems. He currently serves as the Editor-in-Chief of the ACM Transactions on Management Information Systems.