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Self-Regulated Learning (SRL) enhances outcomes but remains challenging to support in digital environments due to the complexity of real-time evaluation. While Reinforcement Learning (RL) enables personalised scaffolding based on students’ SRL strategies, it requires modelling per student, making it impractical for real-time settings. We study Transfer Reinforcement Learning (TRL) as a scalable framework, enabling transfer of optimal strategies across contexts defined by learning outcomes. Using data from 92 secondary school students completing a writing task, we analysed strategies of those with the highest scores and gains to assess transfer feasibility. Successor Representations were implemented to evaluate the impact of leveraging prior SRL strategies. Results reveal notable similarities across contexts, indicating potential for reuse. Moreover, Successor Representations enabled effective knowledge transfer, outperforming classical RL and eliminating retraining. These findings demonstrate TRL’s potential for scalable, personalised SRL support.

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Transfer Reinforcement Learning for Self-Regulated Learning Support: An Evaluation Using Successor Representations

  • 会议论文

  • 发表日期：

    2025年

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  • Kiyoshige Garces,
  • Gloria Milena Fernandez-Nieto,
  • Mladen Rakovic,
  • Xinyu Li,
  • Tongguang Li,
  • Linxuan Zhao,
  • Dragan Gašević,
  • Junyu Xuan,
  • Hua Zuo

摘要

Self-Regulated Learning (SRL) enhances outcomes but remains challenging to support in digital environments due to the complexity of real-time evaluation. While Reinforcement Learning (RL) enables personalised scaffolding based on students’ SRL strategies, it requires modelling per student, making it impractical for real-time settings. We study Transfer Reinforcement Learning (TRL) as a scalable framework, enabling transfer of optimal strategies across contexts defined by learning outcomes. Using data from 92 secondary school students completing a writing task, we analysed strategies of those with the highest scores and gains to assess transfer feasibility. Successor Representations were implemented to evaluate the impact of leveraging prior SRL strategies. Results reveal notable similarities across contexts, indicating potential for reuse. Moreover, Successor Representations enabled effective knowledge transfer, outperforming classical RL and eliminating retraining. These findings demonstrate TRL’s potential for scalable, personalised SRL support.