Article: Personalised learning path recommendation and knowledge tracing model for large-scale online education Journal: International Journal of Information and Communication Technology (IJICT) 2026 Vol.27 No.56 pp.52 - 70 Abstract: This study proposes a unified framework that jointly models personalised learning path recommendation and knowledge tracing to improve individualised learning support in large-scale online education. The framework integrates learners' knowledge states, prerequisite relationships, learning load, and preferences within a single space, enabling dynamic tracking and coordinated optimisation. An online-updatable knowledge tracing model captures mastery levels, which inform a scoring and recommendation mechanism that adapts as knowledge states evolve. Experiments on the EdNet-KT1 dataset show the proposed model achieves superior prediction accuracy and lower mean absolute error than recent baselines, with reduced parameters and training time. This approach balances predictive performance and computational efficiency, offering a practical solution for personalised learning support. Inderscience Publishers - linking academia, business and industry through research

Title: Personalised learning path recommendation and knowledge tracing model for large-scale online education

Authors: Yanhong Song

Addresses: Computer Science School of Wuhan Vocational College of Software and Engineering, Wuhan Open University, Wuhan, 430205, Hubei, China

Abstract: This study proposes a unified framework that jointly models personalised learning path recommendation and knowledge tracing to improve individualised learning support in large-scale online education. The framework integrates learners' knowledge states, prerequisite relationships, learning load, and preferences within a single space, enabling dynamic tracking and coordinated optimisation. An online-updatable knowledge tracing model captures mastery levels, which inform a scoring and recommendation mechanism that adapts as knowledge states evolve. Experiments on the EdNet-KT1 dataset show the proposed model achieves superior prediction accuracy and lower mean absolute error than recent baselines, with reduced parameters and training time. This approach balances predictive performance and computational efficiency, offering a practical solution for personalised learning support.

Keywords: large-scale online education; knowledge tracing; personalised learning path recommendation; deep learning; educational data mining.

DOI: 10.1504/IJICT.2026.153909

International Journal of Information and Communication Technology, 2026 Vol.27 No.56, pp.52 - 70

Received: 08 Jan 2026
Accepted: 02 Mar 2026
Published online: 29 May 2026 *

Title: Personalised learning path recommendation and knowledge tracing model for large-scale online education

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