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Activity Number: 306 - SPEED: SPAAC SESSION II
Type: Topic-Contributed
Date/Time: Wednesday, August 11, 2021 : 3:30 PM to 5:20 PM
Sponsor: Section on Statistical Learning and Data Science
Abstract #318353
Title: A Reinforcement Learning Algorithm for Online Personalized Tutor Recommendation
Author(s): Mohamad Kazem Shirani Faradonbeh*
Companies: University of Georgia
Keywords: reinforcement learning; computerized education; intelligent tutoring; data-driven recommendation; statistical machine learning; decision-making algorithms

Data-driven computerized education platforms can drastically reduce costs of tutoring by statistically learning from the trajectories of the students. We present a reinforcement learning algorithm that is implemented in an online platform for recommending tutoring videos that are personalized to each student. For this purpose, multiple important challenges are addressed. First, the experiments for collecting data need to be diverse for exploring student responses, while at the same time are required to focus on the immediate weakness of each student. Moreover, the number of tutoring items is remarkably large, but each student provides an extremely small data because (s)he can engage in only a few items. Further challenges as well as employed methods that utilize student backgrounds for combining the data, yet recommending personalized tutoring, will be discussed.

Authors who are presenting talks have a * after their name.

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