Article: Big data-driven personalised lifelong learning model for English education Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.9 pp.312 - 337 Abstract: This paper constructed a big data-driven personalised lifelong learning model for English education. By continuously collecting learning behaviour and background data, clustering and learner portrait modelling methods are used to identify individual differences, and dynamic path planning and recommendation mechanisms are combined to generate personalised learning tasks. A feedback control mechanism is introduced to achieve real-time optimisation of the learning process. Experimental results show that the average completeness rate of learning paths based on this model reached 92.94%, the average resource matching degree reached 88.3%, the average task completion rate was above 86%, and the interruption rate of continuous learning behaviour was controlled within 11.4%. Research shows that this model has a significant effect in improving the continuity of the learning process and the accuracy of resource matching, and provides a feasible technical path for the personalised development of English education under the lifelong learning system. Inderscience Publishers - linking academia, business and industry through research

Title: Big data-driven personalised lifelong learning model for English education

Authors: Hao Ren; Hua Sun; Lei Zhu; Yan Gao; Yuan Liu

Addresses: School of Humanities and Education, Xijing University, Xi'an, 710123, Shaanxi, China ' School of Humanities and Social Sciences, Beijing University of Civil Engineering and Architecture, Beijing, 100044, China ' School of Business Administration, Xi'an Eurasia University, Xi'an, 710065, Shaanxi, China ' School of Digital Business, Shaanxi Technical College of Finance and Economics, Xianyang, 712099, Shaanxi, China ' School of Business Administration, Xi'an Eurasia University, Xi'an, 710065, Shaanxi, China

Abstract: This paper constructed a big data-driven personalised lifelong learning model for English education. By continuously collecting learning behaviour and background data, clustering and learner portrait modelling methods are used to identify individual differences, and dynamic path planning and recommendation mechanisms are combined to generate personalised learning tasks. A feedback control mechanism is introduced to achieve real-time optimisation of the learning process. Experimental results show that the average completeness rate of learning paths based on this model reached 92.94%, the average resource matching degree reached 88.3%, the average task completion rate was above 86%, and the interruption rate of continuous learning behaviour was controlled within 11.4%. Research shows that this model has a significant effect in improving the continuity of the learning process and the accuracy of resource matching, and provides a feasible technical path for the personalised development of English education under the lifelong learning system.

Keywords: lifelong learning; personalised English education; big data; learner modelling; learning path optimisation.

DOI: 10.1504/IJCEELL.2026.153629

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.9, pp.312 - 337

Received: 17 Jul 2025
Accepted: 06 Feb 2026
Published online: 18 May 2026 *

Title: Big data-driven personalised lifelong learning model for English education

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