Article: Student behaviour prediction and learning path optimisation in online education platform based on Dijkstra-ACO Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.52 pp.75 - 95 Abstract: With the rapid development of online education, improving students' learning efficiency and experience has become a key research area. This study aims to address the challenges of predicting student behaviour and optimising learning paths on online education platforms. We propose a patented model that combines Dijkstra's algorithm with the ant colony optimisation algorithm to predict student behaviour and optimise learning paths. The experimental results show that the model significantly improves the prediction accuracy, with an accuracy rate of 85.3%. In addition, after path optimisation, the learning efficiency increased by 20%, proving the effectiveness of the model in improving student performance. This study contributes to the development of personalised teaching methods by optimising students' learning paths through the use of intelligent algorithms and presents a patented solution for the intelligent development of online education platforms. Inderscience Publishers - linking academia, business and industry through research

Title: Student behaviour prediction and learning path optimisation in online education platform based on Dijkstra-ACO

Authors: Jiasheng Ma

Addresses: Zhengzhou University of Science and Technology, Zhengzhou, 450000, China

Abstract: With the rapid development of online education, improving students' learning efficiency and experience has become a key research area. This study aims to address the challenges of predicting student behaviour and optimising learning paths on online education platforms. We propose a patented model that combines Dijkstra's algorithm with the ant colony optimisation algorithm to predict student behaviour and optimise learning paths. The experimental results show that the model significantly improves the prediction accuracy, with an accuracy rate of 85.3%. In addition, after path optimisation, the learning efficiency increased by 20%, proving the effectiveness of the model in improving student performance. This study contributes to the development of personalised teaching methods by optimising students' learning paths through the use of intelligent algorithms and presents a patented solution for the intelligent development of online education platforms.

Keywords: Dijkstra algorithm; ant colony optimisation algorithm; student behaviour prediction; learning path optimisation.

DOI: 10.1504/IJICT.2025.151169

International Journal of Information and Communication Technology, 2025 Vol.26 No.52, pp.75 - 95

Received: 19 Sep 2025
Accepted: 13 Nov 2025
Published online: 15 Jan 2026 *

Title: Student behaviour prediction and learning path optimisation in online education platform based on Dijkstra-ACO

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