Computer Science ›› 2026, Vol. 53 ›› Issue (2): 1-15.doi: 10.11896/jsjkx.250700184

• Educational Data Mining Based on Graph Machine Learning • Previous Articles     Next Articles

Review of Personalized Educational Resource Recommendations

XI Penghui1,2, WU Xiazhen1,2, JIANG Wencong1,2, FANG Liangda1,2, HE Chaobo3, GUAN Quanlong1,2   

  1. 1 College of Information Science and Technology,Jinan University,Guangzhou 510632,China
    2 Guangdong Institute of Smart Education,Jinan University,Guangzhou 510632,China
    3 School of Computer Science,South China Normal University,Guangzhou 510631,China
  • Received:2025-07-30 Revised:2025-10-27 Online:2026-02-15 Published:2026-02-10
  • About author:XI Penghui,born in 1996,Ph.D.His main research interests include intelligent education and graph neural network.
    GUAN Quanlong,born in 1981,professor,Ph.D supervisor,is a member of CCF(No.48326D).His main research interests include network security and intelligent education.
  • Supported by:
    National Key R&D Program of China(2022YFC3303603),National Natural Science Foundation of China(62377028) and Fundamental Research Funds for the Central Universities(21625102).

Abstract: Under the background of the “Double Reduction” policy and the ongoing digital transformation of education,persona-lized educational recommender systems(ERSs) have become a key enabler of smart education.By modelling learners’ knowledge mastery,interests,and behavioural patterns,ERS supports personalised instruction and improves learning efficiency.This paper provides a systematic review of research progress in three core tasks:course recommendation,exercise recommendation,and learning path recommendation.Course recommendation has evolved from traditional collaborative filtering and matrix factorisation to graph neural networks and reinforcement learning,enhancing accuracy and adaptability.Exercise recommendation has shifted from static tag matching to dynamic knowledge tracing and deep learning models,capturing complex learner-item interactions.Learning path recommendation must balance knowledge dependency,learner ability evolution,and multi-objective constraints.Recent approaches integrate graph-based modelling,reinforcement learning,and evolutionary algorithms to optimise personalised paths.The paper also reviews mainstream datasets and performance comparisons,summarising the strengths and limitations of different methods.Finally,it highlights future directions:dynamic knowledge evolution modelling,cross-scenario generalisation,adaptive strategy design,and enhanced interpretability and usability,aiming to transform ERS from static and opaque “black-box” models into dynamic,transparent,and human-centred educational ecosystems.

Key words: Educational data mining, Recommender systems, Deep learning, Knowledge graphs, Personalised education

CLC Number: 

  • G434
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