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
XI Penghui1,2, WU Xiazhen1,2, JIANG Wencong1,2, FANG Liangda1,2, HE Chaobo3, GUAN Quanlong1,2
CLC Number:
| [1]URDANETA-PONTE M C,MENDEZ-ZORRILLA A,OLEA-GORDIA-RUIZ I.Recommendation systems for education:Systematic review[J].Electronics,2021,10(14):1611. [2]CHIU T K F,XIA Q,ZHOU X,et al.Systematic literature review on opportunities,challenges,and future research recommendations of artificial intelligence in education[J].Computers and Education:Artificial Intelligence,2023,4:100118. [3]KUNDU S S,SARKAR D,JANA P,et al.Personalization ineducation using recommendation system:an overview[M]//Computational Intelligence in Digital Pedagogy.2020:85-111. [4]BENHAMDI S,BABOURI A,CHIKY R.Personalized recommender system for e-Learning environment[J].Education and Information Technologies,2017,22:1455-1477. [5]YANG K,RAKOVIĆ M,LI Y,et al.Unveiling the tapestry ofautomated essay scoring:A comprehensive investigation of accuracy,fairness,and generalizability[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2024:22466-22474. [6]LABARTHE H,BACHELET R,BOUCHET F,et al.Increa-sing MOOC completion rates through social interactions:a re-commendation system[C]//EMOOCS 2016 Conference.Fourth European MOOCs Stakeholders Summit.2016:471-480. [7]ZHANG T,YUAN B.Visualizing MOOC user behaviors:A case study on XuetangX[C]//International Conference on Intelligent Data Engineering and Automated Learning.Cham:Springer,2016:89-98. [8]WEI X,SUN S,WU D,et al.Personalized online learning re-source recommendation based on artificial intelligence and educational psychology[J].Frontiers in psychology,2021,12:767837. [9]DUAN X,TAN D,FANG L,et al.Reason-and-execute promp-ting:Enhancing multi-modal large language models for solving geometry questions[C]//Proceedings of the 32nd ACM International Conference on Multimedia.2024:6959-6968. [10]ALYOUSSEF I Y.Massive open online course(MOOCs) acceptance:The role of task-technology fit(TTF) for higher education sustainability[J].Sustainability,2021,13(13):7374. [11]ABDELRAHMAN G,WANG Q,NUNES B.Knowledge tracing:A survey[J].ACM Computing Surveys,2023,55(11):1-37. [12]ZHAO J,MAO H,MAO P,et al.Learning path planning me-thods based on learning path variability and ant colony optimization[J].Systems and Soft Computing,2024,6:200091. [13]TAKAMI K,FLANAGAN B.Toward educational explainablerecommender system:explanation generation based on Bayesian knowledge tracing parameters[C]//International Conference on Computers in Education.2021. [14]WANG X,MA W,GUO L,et al.HGNN:Hyperedge-basedgraph neural network for MOOC course recommendation[J].Information Processing & Management,2022,59(3):102938. [15]BI H,CHEN E,HE W,et al.BETA-CD:A Bayesian meta-learned cognitive diagnosis framework for personalized learning[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:5018-5026. [16]WANG F,GAO W,LIU Q,et al.A survey of models for cognitive diagnosis:New developments and future directions[J].ar-Xiv:2407.05458,2024. [17]HOGAN A,BLOMQVIST E,COCHEZ M,et al.Knowledgegraphs[J].ACM Computing Surveys,2021,54(4):1-37. [18]CANESE L,CARDARILLI G C,DI NUNZIO L,et al.Multi-agent reinforcement learning:A review of challenges and applications[J].Applied Sciences,2021,11(11):4948. [19]CAO J,FANG J,MENG Z,et al.Knowledge graph embedding:A survey from the perspective of representation spaces[J].ACM Computing Surveys,2024,56(6):1-42. [20]LIU F,HU X,LIU S,et al.Meta multi-agent exercise recommendation:A game application perspective[C]//Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2023:1441-1452. [21]YU P,LIU X,CHENG H,et al.A review of online course re-commendation systems[J].Computer Engineering and Applications,2023,59(22):1-14. [22]JIANG L,LIU K,WANG Y,et al.Reinforced explainableknowledge concept recommendation in MOOCs[J].ACM Transactions on Intelligent Systems and Technology,2023,14(3):1-20. [23]PARAMESWARAN A,VENETIS P,GARCIA-MOLINA H.Recommendation systems with complex constraints:A course recommendation perspective[J].ACM Transactions on Information Systems,2011,29(4):1-33. [24]AHER S B,LOBO L.Combination of machine learning algo-rithms for recommendation of courses in E-Learning System based on historical data[J].Knowledge-Based Systems,2013,51:1-14. [25]JING X,TANG J.Guess you like:course recommendation in MOOCs[C]//Proceedings of the International Conference on Web Intelligence.2017:783-789. [26]CHANG P C,LIN C H,CHEN M H.A hybrid course recommendation system by integrating collaborative filtering and artificial immune systems[J].Algorithms,2016,9(3):47. [27]ELBADRAWY A,KARYPIS G.Domain-aware grade prediction and top-n course recommendation[C]//Proceedings of the 10th ACM Conference on Recommender Systems.2016:183-190. [28]VEDAVATHI N,KM A K.E-learning course recommendation based on sentiment analysis using hybrid Elman similarity[J].Knowledge-Based Systems,2023,259:110086. [29]KABBUR S,NING X,KARYPIS G.Fism:factored item simila-rity models for top-n recommender systems[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2013:659-667. [30]JUNG H,JANG Y,KIM S,et al.KPCR:Knowledge graph enhanced personalized course recommendation[C]//Australasian Joint Conference on Artificial Intelligence.Cham:Springer,2022:739-750. [31]YANG S,CAI X.Bilateral knowledge graph enhanced onlinecourse recommendation[J].Information Systems,2022,107:102000. [32]LI M,LI Z,HUANG C,et al.Edugraph:Learning path-based hypergraph neural networks for mooc course recommendation[J].IEEE Transactions on Big Data,2024,10(6):706-719. [33]DENG W,ZHU P,CHEN H,et al.Knowledge-aware sequence modelling with deep learning for online course recommendation[J].Information Processing & Management,2023,60(4):103377. [34]SUN J,MEI S,YUAN K,et al.Prerequisite-enhanced category-aware graph neural networks for course recommendation[J].ACM Transactions on Knowledge Discovery from Data,2024,18(5):1-21. [35]ZHANG Y,YU M,SUN J,et al.Mg-cr:factor memory network and graph neural network based personalized course recommendation[C]//International Conference on Database Systems for Advanced Applications.Cham:Springer,2023:547-562. [36]YANG Y,ZHANG C,SONG X,et al.Contextualized knowledge graph embedding for explainable talent training course recommendation[J].ACM Transactions on Information Systems,2023,42(2):1-27. [37]ZHANG J,HAO B,CHEN B,et al.Hierarchical reinforcement learning for course recommendation in MOOCs[C]//Procee-dings of the AAAI Conference on Artificial Intelligence.2019:435-442. [38]LIN Y,LIN F,ZENG W,et al.Hierarchical reinforcement lear-ning with dynamic recurrent mechanism for course recommendation[J].Knowledge-Based Systems,2022,244:108546. [39]WANG L,ZHANG W,HE X,et al.Supervised reinforcement learning with recurrent neural network for dynamic treatment recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mi-ning.2018:2447-2456. [40]TIAN X,LIU F.Capacity tracing-enhanced course recommendation in MOOCs[J].IEEE Transactions on Learning Technologies,2021,14(3):313-321. [41]JIANG L,XIAO Y,ZHAO X,et al.Hierarchical reinforcement learning on multi-channel hypergraph neural network for course recommendation[C]//Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence(IJCAI-24).2024. [42]LIN Y,FENG S,LIN F,et al.Adaptive course recommendation in MOOCs[J].Knowledge-Based Systems,2021,224:107085. [43]PARDOS Z A,JIANG W.Designing for serendipity in a university course recommendation system[C]//Proceedings of the Tenth International Conference on Learning Analytics & Knowledge.2020:350-359. [44]RAO S,SALOMATIN K,POLATKAN G,et al.Learning to be relevant:evolution of a course recommendation system[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:2625-2633. [45]LI S,ZHAO Y,GUO L,et al.Quantification and prediction of engagement:Applied to personalized course recommendation to reduce dropout in MOOCs[J].Information Processing & Ma-nagement,2024,61(1):103536. [46]BAN Q,WU W,HU W,et al.Knowledge-enhanced multi-task learning for course recommendation[C]//International Confe-rence on Database Systems for Advanced Applications.Cham:Springer,2022:85-101. [47]HAO P,LI Y,BAI C.Meta-relationship for course recommendation in MOOCs[J].Multimedia Systems,2023,29(1):235-246. [48]ZHU Y,LU H,QIU P,et al.Heterogeneous teaching evaluation network based offline course recommendation with graph lear-ning and tensor factorization[J].Neurocomputing,2020,415:84-95. [49]MA B,TANIGUCHI Y,KONOMI S.Course Recommendation for University Environments[C]//Proceedings of the 13th International Educational Data Mining.2020:460-466. [50]WANG C,ZHU H,ZHU C,et al.Personalized employee trai-ning course recommendation with career development awareness[C]//Proceedings of the Web Conference 2020.2020:1648-1659. [51]GEORGE G,LAL A M.A personalized approach to course re-commendation in higher education[J].International Journal on Semantic Web and Information Systems,2021,17(2):100-114. [52]XIONG Z,LI H,LIU Z,et al.A review of data mining in personalized education:Current trends and future prospects[J].Frontiers of Digital Education,2024,1(1):26-50. [53]WU Z,LI M,TANG Y,et al.Exercise recommendation based on knowledge concept prediction[J].Knowledge-Based Systems,2020,210:106481. [54]GAO W W,MA H F,ZHAO Y,et al.Enhancing personalized exercise recommendation with student and exercise portraits[J].Journal of Electronic Science and Technology,2024,22(2):100262. [55]HUO Y,WONG D F,NI L M,et al.Knowledge modeling via contextualized representations for LSTM-based personalized exercise recommendation[J].Information Sciences,2020,523:266-278. [56]AI F,CHEN Y,GUO Y,et al.Concept-aware deep knowledge tracing and exercise recommendation in an online learning system[C]//Proceedings of the 12th International Educational Data Mining.2019:240-245. [57]GONG T,YAO X.Deep exercise recommendation model[J].International Journal of Modeling and Optimization,2019,9(1):18-23. [58]HUO Y,XIAO J,NI L M.Towards personalized learningthrough class contextual factors-based exercise recommendation[C]//2018 IEEE 24th International Conference on Parallel and Distributed Systems(ICPADS).IEEE,2018:85-92. [59]KOREN Y,RENDLE S,BELL R.Advances in collaborative filtering[M]//Recommender Systems Handbook.2021:91-142. [60]LI Z,HU H,XIA Z,et al.Exercise recommendation methodbased on machine learning[C]//2021 International Conference on Advanced Learning Technologies(ICALT).IEEE,2021:50-52. [61]ZHENG W,DU Q,FAN Y,et al.A personalized programming exercise recommendation algorithm based on knowledge structure tree[J].Journal of Intelligent & Fuzzy Systems,2022,42(3):2169-2180. [62]GUAN Q,XIAO F,CHENG X,et al.Kg4ex:An explainableknowledge graph-based approach for exercise recommendation[C]//Proceedings of the 32nd ACM International Conference on Information and Knowledge Management.2023:597-607. [63]ZHU M,ZHEN D,TAO R,et al.Top-N collaborative filtering recommendation algorithm based on knowledge graph embe-dding[C]//International Conference on Knowledge Management in Organizations.Cham:Springer,2019:122-134. [64]CHENG X,ZHOU X,FANG L,et al.NR4DER:Neural Re-ranking for Diversified Exercise Recommendation[C]//Procee-dings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval.2025:1738-1747. [65]YAN Z,DU H,LIN Z,et al.Personalization exercise recommendation framework based on knowledge concept graph[J].Computer Science and Information Systems,2023,20(2):857-878. [66]LYU P,WANG X,XU J,et al.Intelligent personalised exercise recommendation:A weighted knowledge graph-based approach[J].Computer Applications in Engineering Education,2021,29(5):1403-1419. [67]GUAN Q,CHENG X,XIAO F,et al.Explainable exercise re-commendation with knowledge graph[J].Neural Networks,2025,183:106954. [68]LIU G,REN M,GUO L,et al.Comprehensive exercise recommendation with practicality,generalizability,and versatility in AI-driven education[J].Information Processing & Management,2025,62(3):104051. [69]WU S,WANG J,ZHANG W.Contrastive personalized exercise recommendation with reinforcement learning[J].IEEE Transactions on Learning Technologies,2023,17:691-703. [70]HUANG Z,LIU Q,ZHAI C,et al.Exploring multi-objectiveexercise recommendations in online education systems[C]//Proceedings of the 28th ACM International Conference on Information and Knowledge Management.2019:1261-1270. [71]REN Y,LIANG K,SHANG Y,et al.MulOER-SAN:2-layermulti-objective framework for exercise recommendation with self-attention networks[J].Knowledge-Based Systems,2023,260:110117. [72]THURMOND V,WAMBACH K.Understanding interactions in distance education:A review of the literature[J].International Journal of Instructional Technology and Distance Learning,2004,1(1):9-26. [73]SHI D,WANG T,XING H,et al.A learning path recommendation model based on a multidimensional knowledge graph framework for e-learning[J].Knowledge-Based Systems,2020,195:105618. [74]ZHU H,TIAN F,WU K,et al.A multi-constraint learning path recommendation algorithm based on knowledge map[J].Know-ledge-Based Systems,2018,143:102-114. [75]ZHANG X,LIU S,WANG H.Personalized learning path recommendation for e-learning based on knowledge graph and graph convolutional network[J].International Journal of Software Engineering and Knowledge Engineering,2023,33(1):109-131. [76]CHEN H,YIN C,FAN X,et al.Learning path recommendation for MOOC platforms based on a knowledge graph[C]//Know-ledge Science,Engineering and Management:14th International Conference,KSEM 2021.2021:600-611. [77]FIQRI M,NURJANAH D.Graph-based domain model for adaptive learning path recommendation[C]//2017 IEEE Global Engineering Education Conference(EDUCON).IEEE,2017:375-380. [78]CHENG Y.A learning path recommendation method for know-ledge graph of professional courses[C]//2022 IEEE 22nd International Conference on Software Quality,Reliability,and Security Companion(QRS-C).IEEE,2022:469-476. [79]YIN H,SUN Z,SUN Y,et al.Automatic learning path recommendation for open source projects using deep learning on knowledge graphs[C]//2021 IEEE 45th Annual Computers,Software,and Applications Conference(COMPSAC).IEEE,2021:824-833. [80]TANG C L,LIAO J,WANG H C,et al.Conceptguide:Suppor-ting online video learning with concept map-based recommendation of learning path[C]//Proceedings of the Web Conference 2021.2021:2757-2768. [81]TANG C L,LIAO J,WANG H C,et al.Supporting online video learning with concept map-based recommendation of learning path[C]//Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems.2020:1-8. [82]LIU Z,CHEN B,ZHOU H,et al.Mapper:Multi-agent pathplanning with evolutionary reinforcement learning in mixed dynamic environments[C]//2020 IEEE/RSJ International Confe-rence on Intelligent Robots and Systems(IROS).IEEE,2020:11748-11754. [83]GUAN Q,YU Y,HUANG X,et al.Generating Privacy-preserving Educational Data Records with Diffusion Model[C]//Companion Proceedings of the ACM Web Conference 2024.2024:806-809. [84]CAI D,ZHANG Y,DAI B.Learning path recommendationbased on knowledge tracing model and reinforcement learning[C]//2019 IEEE 5th International Conference on Computer and Communications(ICCC).IEEE,2019:1881-1885. [85]YUN Y,DAI H,AN R,et al.Doubly constrained offline reinforcement learning for learning path recommendation[J].Knowledge-Based Systems,2024,284:111242. [86]LI Q,XIA W,YIN L,et al.Graph enhanced hierarchical reinforcement learning for goal-oriented learning path recommendation[C]//Proceedings of the 32nd ACM International Confe-rence on Information and Knowledge Management.2023:1318-1327. [87]DWIVEDI P,KANT V,BHARADWAJ K K.Learning path re-commendation based on modified variable length genetic algorithm[J].Education and information technologies,2018,23:819-836. [88]ZHENG Y,WANG D,XU Y,et al.A Multigranularity Lear-ning Path Recommendation Framework Based on Knowledge Graph and Improved Ant Colony Optimization Algorithm for E-Learning[J].IEEE Transactions on Computational Social Systems,2024,12(2):586-607. [89]BIAN C L,WANG D L,LIU S Y,et al.Adaptive learning path recommendation based on graph theory and an improved immune algorithm[J].KSII Transactions on Internet and Information Systems,2019,13(5):2277-2298. [90]NIKNAM M,THULASIRAMAN P.LPR:A bio-inspired intelligent learning path recommendation system based on meaningful learning theory[J].Education and Information Technologies,2020,25(5):3797-3819. [91]CHANG H S,HSU H J,CHEN K T.Modeling exercise relationships in E-learning:A unified approach[C]//EDM.2015:532-535. [92]MA D,ZHU H,LIAO S,et al.Learning path recommendation with multi-behavior user modeling and cascading deep Q networks[J].Knowledge-Based Systems,2024,294:111743. [93]ZHANG S,HUI N,ZHAI P,et al.A fine-grained and multi-context-aware learning path recommendation model over know-ledge graphs for online learning communities[J].Information Processing & Management,2023,60(5):103464. [94]HIDASI B,KARATZOGLOU A,BALTRUNAS L,et al.Session-based recommendations with recurrent neural networks[J].arXiv:1511.06939,2015. [95]MA Y,WANG L,ZHANG J,et al.A personalized learning path recommendation method incorporating multi-algorithm[J].Applied Sciences,2023,13(10):5946. [96]RAJ N S,RENUMOL V G.An improved adaptive learning path recommendation model driven by real-time learning analytics[J].Journal of Computers in Education,2024,11(1):121-148. [97]ZHANG H,SHEN S,XU B,et al.Item-Difficulty-Aware Lear-ning Path Recommendation:From a Real Walking Perspective[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2024:4167-4178. [98]LIU H,LI X.Learning path combination recommendation based on the learning networks[J].Soft Computing,2020,24(6):4427-4439. [99]CHEN X,SHEN J,XIA W,et al.Set-to-sequence ranking-based concept-aware learning path recommendation[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2023:5027-5035. [100]FREJ J,SHAH N,KNEZEVIC M,et al.Finding paths for explainable mooc recommendation:A learner perspective[C]//Proceedings of the 14th Learning Analytics and Knowledge Conference.2024:426-437. [101]ZHANG F,FENG X,WANG Y.Personalized process-typelearning path recommendation based on process mining and deep knowledge tracing[J].Knowledge-Based Systems,2024,303:112431. [102]LUO G,GU H,DONG X,et al.HA-LPR:A highly adaptivelearning path recommendation[J].Education and Information Technologies,2025,30:14597-14627. [103]LI Y,SHAN Z,RAKOVIĆ M,et al.When AI explains in natural language:Unveiling the impact of generative AI explanations on educators’ grading and feedback practices[EB/OL].https://doi.org/10.1007/s10639-025-13741-z. [104]LI Y,RAKOVIĆ M,SRIVASTAVA N,et al.Can AI support human grading? Examining machine attention and confidence in short answer scoring[J].Computers & Education,2025,228:105244. [105]LI L,SRIVASTAVA N,RONG J,et al.When and how biases seep in:Enhancing debiasing approaches for fair educational predictive analytics[J].British Journal of Educational Technology,2025,56(6):2478-2501. |
| [1] | HUANG Jing, WANG Teng, LIU Jian, HU Kai, PENG Xin, HUANG Yamin, WEN Yuanqiao. Multimodal Visual Detection for Underwater Sonar Target Images [J]. Computer Science, 2026, 53(2): 227-235. |
| [2] | LIU Chenhong, LI Fenglian, YANG Jia, WANG Suzhe, CHEN Guijun. Boundary-focused Multi-scale Feature Fusion Network for Stroke Lesion Segmentation [J]. Computer Science, 2026, 53(2): 264-272. |
| [3] | ZHAI Jie, CHEN Lexuan, PANG Zhiyu. Survey on Graph Neural Network-based Methods for Academic Performance Prediction [J]. Computer Science, 2026, 53(2): 16-30. |
| [4] | HUANG Miaomiao, WANG Huiying, WANG Meixia, WANG Yejiang , ZHAO Yuhai. Review of Graph Embedding Learning Research:From Simple Graph to Complex Graph [J]. Computer Science, 2026, 53(1): 58-76. |
| [5] | WANG Cheng, JIN Cheng. KAN-based Unsupervised Multivariate Time Series Anomaly Detection Network [J]. Computer Science, 2026, 53(1): 89-96. |
| [6] | XUE Jingyan, XIA Jianan, HUO Ruili, LIU Jie, ZHOU Xuezhong. Review of Retinal Image Analysis Methods for OCT/OCTA Based on Deep Learning [J]. Computer Science, 2026, 53(1): 128-140. |
| [7] | ZHOU Bingquan, JIANG Jie, CHEN Jiangmin, ZHAN Lixin. EvR-DETR:Event-RGB Fusion for Lightweight End-to-End Object Detection [J]. Computer Science, 2026, 53(1): 153-162. |
| [8] | YIN Shi, SHI Zhenyang, WU Menglin, CAI Jinyan, YU De. Deep Learning-based Kidney Segmentation in Ultrasound Imaging:Current Trends and Challenges [J]. Computer Science, 2025, 52(9): 16-24. |
| [9] | ZENG Lili, XIA Jianan, LI Shaowen, JING Maike, ZHAO Huihui, ZHOU Xuezhong. M2T-Net:Cross-task Transfer Learning Tongue Diagnosis Method Based on Multi-source Data [J]. Computer Science, 2025, 52(9): 47-53. |
| [10] | LI Yaru, WANG Qianqian, CHE Chao, ZHU Deheng. Graph-based Compound-Protein Interaction Prediction with Drug Substructures and Protein 3D Information [J]. Computer Science, 2025, 52(9): 71-79. |
| [11] | LUO Chi, LU Lingyun, LIU Fei. Partial Differential Equation Solving Method Based on Locally Enhanced Fourier NeuralOperators [J]. Computer Science, 2025, 52(9): 144-151. |
| [12] | LIU Leyuan, CHEN Gege, WU Wei, WANG Yong, ZHOU Fan. Survey of Data Classification and Grading Studies [J]. Computer Science, 2025, 52(9): 195-211. |
| [13] | LIU Wei, XU Yong, FANG Juan, LI Cheng, ZHU Yujun, FANG Qun, HE Xin. Multimodal Air-writing Gesture Recognition Based on Radar-Vision Fusion [J]. Computer Science, 2025, 52(9): 259-268. |
| [14] | TANG Boyuan, LI Qi. Review on Application of Spatial-Temporal Graph Neural Network in PM2.5 ConcentrationForecasting [J]. Computer Science, 2025, 52(8): 71-85. |
| [15] | LIU Zhengyu, ZHANG Fan, QI Xiaofeng, GAO Yanzhao, SONG Yijing, FAN Wang. Review of Research on Deep Learning Compiler [J]. Computer Science, 2025, 52(8): 29-44. |
|
||