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A long-standing challenge of intelligent tutoring systems (ITSs) is to determine when to provide what types of problems to learners based on their individual needs. In this study, we investigate a DRL-based adaptive pedagogical policy for choosing problem types among problem solving, worked examples, and Parsons’ problems in a data-driven intelligent logic tutor. We compare the DRL-based policy with a non-adaptive expert policy and a problem-solving-only control on learning gain and completion time. Overall, results show that the DRL policy did help adapt the tutor in some ways, significantly improving performance on one post-test problem and marginally reducing post-test time for low prior proficiency learners.

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Determining Problem Type Using Deep Reinforcement Learning in a Data-Driven Intelligent Tutor

  • 会议论文

  • 发表日期：

    2025年

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  • Nazia Alam,
  • Kimia Fazeli,
  • Xiaoyi Tian,
  • Min Chi,
  • Tiffany Barnes

摘要

A long-standing challenge of intelligent tutoring systems (ITSs) is to determine when to provide what types of problems to learners based on their individual needs. In this study, we investigate a DRL-based adaptive pedagogical policy for choosing problem types among problem solving, worked examples, and Parsons’ problems in a data-driven intelligent logic tutor. We compare the DRL-based policy with a non-adaptive expert policy and a problem-solving-only control on learning gain and completion time. Overall, results show that the DRL policy did help adapt the tutor in some ways, significantly improving performance on one post-test problem and marginally reducing post-test time for low prior proficiency learners.