Article: A big data-driven personalised learning framework for education with adaptive content delivery Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.70 pp.97 - 116 Abstract: The increasing demand for sustainable and technology-enabled education underscores the necessity of adaptive learning systems to meet diverse learner needs. Traditional static curricula struggle to support dynamic knowledge domains and personalised learning paths. This study presents a big data-driven personalised learning framework that integrates educational datasets and learner analytics from wearable-enabled environments to dynamically adjust content delivery. Experiments using real educational data and simulated interaction logs show that the proposed framework outperforms conventional static approaches, with a 32.6% improvement in learning gain, a 22% increase in quiz accuracy, and a 50% rise in learner engagement time. Comparative assessments against four existing adaptive models verify its superior effectiveness and robustness. The findings highlight the value of big data analytics and intelligent models in aligning academic learning with evolving industry skills. This framework offers a scalable, sustainable solution for modern education, supporting personalised, adaptive learning tailored to future skill requirements. Inderscience Publishers - linking academia, business and industry through research

Title: A big data-driven personalised learning framework for education with adaptive content delivery

Authors: Yilin Yuan; Kamisah Binti Osman; Yunlei Liao

Addresses: College of Educational Sciences, Lingnan Normal University, Zhanjiang, 524048, Guangdong, China; Faculty of Education, National University of Malaysia, Selangor State, 43600, Malaysia ' Faculty of Education, National University of Malaysia, Selangor State, 43600, Malaysia ' Faculty of Education, National University of Malaysia, Selangor State, 43600, Malaysia

Abstract: The increasing demand for sustainable and technology-enabled education underscores the necessity of adaptive learning systems to meet diverse learner needs. Traditional static curricula struggle to support dynamic knowledge domains and personalised learning paths. This study presents a big data-driven personalised learning framework that integrates educational datasets and learner analytics from wearable-enabled environments to dynamically adjust content delivery. Experiments using real educational data and simulated interaction logs show that the proposed framework outperforms conventional static approaches, with a 32.6% improvement in learning gain, a 22% increase in quiz accuracy, and a 50% rise in learner engagement time. Comparative assessments against four existing adaptive models verify its superior effectiveness and robustness. The findings highlight the value of big data analytics and intelligent models in aligning academic learning with evolving industry skills. This framework offers a scalable, sustainable solution for modern education, supporting personalised, adaptive learning tailored to future skill requirements.

Keywords: big data; personalised learning; adaptive content; educational data analytics; intelligent education.

DOI: 10.1504/IJICT.2026.154393

International Journal of Information and Communication Technology, 2026 Vol.27 No.70, pp.97 - 116

Received: 12 Mar 2026
Accepted: 14 Apr 2026
Published online: 25 Jun 2026 *

Title: A big data-driven personalised learning framework for education with adaptive content delivery

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