Article: Learning path planning method for engineering education based on course group knowledge graph and learner portrait Journal: International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL) 2026 Vol.36 No.11 pp.101 - 125 Abstract: With the increasing demand for personalised learning support and intelligent recommendation in engineering education, traditional learning path planning methods still face challenges in handling multi-source learner profiles and complex course associations, resulting in low path matching rates and high recommendation latency. To address these issues, this study proposes a learning path recommendation framework integrating knowledge graph modelling and multi-source behaviour perception. The framework combines dynamic knowledge tracing with hierarchical regularised user modelling and knowledge graph embedding with an upper confidence bound strategy to achieve accurate path recommendation. Experimental results show that the proposed model achieves an F1-score of 93.22% on a public dataset, while the average response delay is within 2 seconds. Under noise disturbances, the fluctuation of path similarity remains within ±2.5%, and the course completion rate reaches 97.3% across four engineering disciplines, demonstrating strong accuracy, real-time performance and adaptability. Inderscience Publishers - linking academia, business and industry through research

Title: Learning path planning method for engineering education based on course group knowledge graph and learner portrait

Authors: Nanping Wang; Yuanlin Wang; Ming Jiang

Addresses: School of Art and Design, Guilin University of Electronic Technology, Guilin, 541004, China ' School of Marxism, Guilin University of Electronic Technology, Guilin, 541004, China ' School of Computer and Information Security, Guilin University of Electronic Technology, Guilin, 541004, China

Abstract: With the increasing demand for personalised learning support and intelligent recommendation in engineering education, traditional learning path planning methods still face challenges in handling multi-source learner profiles and complex course associations, resulting in low path matching rates and high recommendation latency. To address these issues, this study proposes a learning path recommendation framework integrating knowledge graph modelling and multi-source behaviour perception. The framework combines dynamic knowledge tracing with hierarchical regularised user modelling and knowledge graph embedding with an upper confidence bound strategy to achieve accurate path recommendation. Experimental results show that the proposed model achieves an F1-score of 93.22% on a public dataset, while the average response delay is within 2 seconds. Under noise disturbances, the fluctuation of path similarity remains within ±2.5%, and the course completion rate reaches 97.3% across four engineering disciplines, demonstrating strong accuracy, real-time performance and adaptability.

Keywords: engineering education; learning path planning; knowledge graph; user profiling; intelligent recommendation.

DOI: 10.1504/IJCEELL.2026.154665

International Journal of Continuing Engineering Education and Life-Long Learning, 2026 Vol.36 No.11, pp.101 - 125

Received: 22 Aug 2025
Accepted: 04 Feb 2026
Published online: 09 Jul 2026 *

Title: Learning path planning method for engineering education based on course group knowledge graph and learner portrait

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