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Sequential Decision-Making in Online Learning Engagement among Chinese EFL Learners: A Reinforcement Learning Approach

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Title: Sequential Decision-Making in Online Learning Engagement among Chinese EFL Learners: A Reinforcement Learning Approach
Language: English
Authors: Hong Xie
Source: Smart Learning Environments. 2026 13.
Availability: Springer. Available from: Springer Nature. One New York Plaza, Suite 4600, New York, NY 10004. Tel: 800-777-4643; Tel: 212-460-1500; Fax: 212-460-1700; e-mail: customerservice@springernature.com; Web site: https://link.springer.com/
Peer Reviewed: Y
Page Count: 22
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Descriptors: Sequential Approach, Decision Making, Electronic Learning, Learner Engagement, Second Language Learning, English (Second Language), Foreign Countries, Reinforcement, Self Efficacy, Metacognition, Interpersonal Relationship, Interaction, Rewards
Geographic Terms: China
DOI: 10.1186/s40561-026-00445-1
ISSN: 2196-7091
Abstract: 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.
Abstractor: As Provided
Entry Date: 20260101
Accession Number: EJ1512902
Database: ERIC
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  Data: 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.
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