Leveraging Machine Learning Approach to Identify the Predictors of Informal Digital Learning of English Behaviours Among EFL Learners

IF 4.6 2区教育学 Q1 EDUCATION & EDUCATIONAL RESEARCH

Journal of Computer Assisted Learning Pub Date : 2025-09-07 DOI:10.1111/jcal.70111

Yu Cui, Lingjie Tang, Fang Fang

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引用次数: 0

Abstract

Background Study

With the rapid transition to remote learning necessitated by the closure of traditional educational infrastructures globally, the arena of informal digital learning of English (IDLE) has received much attention, particularly among English as a Foreign Language (EFL) learners in China.

Objective

This study explores how demographic variables (gender, age, grade, major, and background) along with confidence, desire, online self-efficacy, attitudinal belief, and intention to learn English predict IDLE behaviours among EFL learners in IDLE contexts.

Methods

Utilising a comprehensive dataset, the research incorporates machine learning algorithms (e.g., Random Forest, Support Vector Machine, Logistic Regression, Decision Tree, Gradient Boosting Decision Tree and Adaptive Boosting (AdaBoost)) to analyse psychological, behavioural and demographic predictors of IDLE behaviours. Participants included 2, 055 EFL learners in China.

Results

The study finds that EFL learners' confidence, desire, online self-efficacy, attitudinal belief, intention to learn English and IDLE behaviours display a moderate level. Moreover, confidence and desire act as the strongest predictors of IDLE behaviours, whereas demographic variables (gender, age, grade, major and background) predict the minimum of IDLE behaviours.

Conclusion

By understanding these predictors, educational strategies can be better tailored to enhance digital education outcomes.

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利用机器学习方法识别英语学习者非正式数字学习行为的预测因素

随着传统教育基础设施的关闭，全球范围内的远程学习迅速过渡，非正式数字英语学习（IDLE）领域受到了广泛关注，特别是在中国的英语作为外语（EFL）学习者中。目的本研究探讨人口统计学变量（性别、年龄、年级、专业和背景）以及自信、渴望、在线自我效能感、态度信念和学习英语意愿如何预测英语学习者在IDLE情境下的IDLE行为。方法利用一个全面的数据集，研究结合了机器学习算法（如随机森林、支持向量机、逻辑回归、决策树、梯度增强决策树和自适应增强（AdaBoost））来分析IDLE行为的心理、行为和人口预测因素。参与者包括2055名中国的英语学习者。结果研究发现，英语学习者的自信心、学习欲望、网络自我效能感、态度信念、英语学习意向和IDLE行为表现为中等水平。此外，信心和欲望是IDLE行为的最强预测因子，而人口变量（性别、年龄、年级、专业和背景）预测IDLE行为的最小值。通过了解这些预测因素，可以更好地定制教育策略，以提高数字教育的成果。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computer Assisted Learning EDUCATION & EDUCATIONAL RESEARCH-

CiteScore

9.70

自引率

6.00%

发文量

116

期刊介绍： The Journal of Computer Assisted Learning is an international peer-reviewed journal which covers the whole range of uses of information and communication technology to support learning and knowledge exchange. It aims to provide a medium for communication among researchers as well as a channel linking researchers, practitioners, and policy makers. JCAL is also a rich source of material for master and PhD students in areas such as educational psychology, the learning sciences, instructional technology, instructional design, collaborative learning, intelligent learning systems, learning analytics, open, distance and networked learning, and educational evaluation and assessment. This is the case for formal (e.g., schools), non-formal (e.g., workplace learning) and informal learning (e.g., museums and libraries) situations and environments. Volumes often include one Special Issue which these provides readers with a broad and in-depth perspective on a specific topic. First published in 1985, JCAL continues to have the aim of making the outcomes of contemporary research and experience accessible. During this period there have been major technological advances offering new opportunities and approaches in the use of a wide range of technologies to support learning and knowledge transfer more generally. There is currently much emphasis on the use of network functionality and the challenges its appropriate uses pose to teachers/tutors working with students locally and at a distance. JCAL welcomes: -Empirical reports, single studies or programmatic series of studies on the use of computers and information technologies in learning and assessment -Critical and original meta-reviews of literature on the use of computers for learning -Empirical studies on the design and development of innovative technology-based systems for learning -Conceptual articles on issues relating to the Aims and Scope