Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task

• URL: http://arxiv.org/abs/2304.04933v2
• Date: Thu, 13 Apr 2023 19:33:51 GMT
• Title: Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task
• Authors: Sherry Ruan, Allen Nie, William Steenbergen, Jiayu He, JQ Zhang, Meng Guo, Yao Liu, Kyle Dang Nguyen, Catherine Y Wang, Rui Ying, James A Landay, Emma Brunskill
• Abstract summary: Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of intelligent tutoring software. We show that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume.
• Score: 32.6507926764587
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Resource limitations make it hard to provide all students with one of the most effective educational interventions: personalized instruction. Reinforcement learning could be a key tool to reduce the development cost and improve the effectiveness of intelligent tutoring software that aims to provide the right support, at the right time, to a student. Here we illustrate that deep reinforcement learning can be used to provide adaptive pedagogical support to students learning about the concept of volume in a narrative storyline software. Using explainable artificial intelligence tools, we extracted interpretable insights about the pedagogical policy learned and demonstrated that the resulting policy had similar performance in a different student population. Most importantly, in both studies, the reinforcement-learning narrative system had the largest benefit for those students with the lowest initial pretest scores, suggesting the opportunity for AI to adapt and provide support for those most in need.

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