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<p>Learning engagement is inherently dynamic, historically dependent, and oriented to long-term utility. Building on sequential decision-making theory, this study simulates the dynamic, long-term evolution of online learning engagement among Chinese EFL learners using reinforcement learning models for the first time. The findings unveil that online learning engagement can be reconceptualized as a sequential decision-making process. In the process, three optimal policy regimes, stable reinforcement strategies, conservative strategies, and proactive strategies, are found. They can explain how learning engagement is sustained, stabilized, or redirected across various learners’ profiles. Furthermore, learner attributes such as online self-efficacy, metacognitive engagement, and social interactions, jointly reconfigure the sequential decision-making process, altering state representations, rewards, and action costs, thereby generating divergent optimal decision-making strategies. Therein, social interaction is the most powerful predictor. Despite its intrinsic limitations, the present study underscores the essential nature of language learning. More critically, it proposes an innovative interdisciplinary framework for SLA research, which synthesizes insights from decision science, cognitive science, machine learning, and SLA itself.</p>

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Sequential decision-making in online learning engagement among Chinese EFL learners: a reinforcement learning approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月18日

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  • Hong Xie

摘要

Learning engagement is inherently dynamic, historically dependent, and oriented to long-term utility. Building on sequential decision-making theory, this study simulates the dynamic, long-term evolution of online learning engagement among Chinese EFL learners using reinforcement learning models for the first time. The findings unveil that online learning engagement can be reconceptualized as a sequential decision-making process. In the process, three optimal policy regimes, stable reinforcement strategies, conservative strategies, and proactive strategies, are found. They can explain how learning engagement is sustained, stabilized, or redirected across various learners’ profiles. Furthermore, learner attributes such as online self-efficacy, metacognitive engagement, and social interactions, jointly reconfigure the sequential decision-making process, altering state representations, rewards, and action costs, thereby generating divergent optimal decision-making strategies. Therein, social interaction is the most powerful predictor. Despite its intrinsic limitations, the present study underscores the essential nature of language learning. More critically, it proposes an innovative interdisciplinary framework for SLA research, which synthesizes insights from decision science, cognitive science, machine learning, and SLA itself.