Leveraging Machine Learning Approach to Identify the Predictors of Informal Digital Learning of English Behaviours Among EFL Learners.
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 Chi...
| Publicado en: | Journal of Computer Assisted Learning Vol. 41; no. 5; pp. 1 - 19 |
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| Autores principales: | , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Oct2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=188234209&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 188234209 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02664909 6M1 jtl: Journal of Computer Assisted Learning issn: 02664909 maglogo: Y pubinfo: dt: Oct2025 vid: 41 iid: 5 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 188234209 188234209 188234209 10.1111/jcal.70111 188234209 ppf: 1 ppct: 18 formats: tig: atl: Leveraging Machine Learning Approach to Identify the Predictors of Informal Digital Learning of English Behaviours Among EFL Learners. aug: au: Cui, Yu Tang, Lingjie Fang, Fang affil: School of Foreign Studies, Xi'an Jiaotong University, Xi'an Shaanxi,, China sug: subj: English as a Second Language China Online Education Machine Learning Algorithms Sex Factors Age Factors Educational Status Confidence Evaluation Students, Undergraduate Psychosocial Factors Students, Graduate Psychosocial Factors Student Attitudes Evaluation Intention Evaluation Digital Technology Educational Technology Self-Efficacy Evaluation China Human Male Female Adolescence Adult Random Forest Support Vector Machine Logistic Regression Decision Trees Boosting Machine Learning Algorithms Ajzen's Theory of Planned Behavior Social Learning Theory Questionnaires Colleges and Universities Summated Rating Scaling Scales Descriptive Statistics Data Analysis Software Cross Sectional Studies Surveys Adolescent: 13-18 years Adult: 19-44 years Male Female ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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