GTKT:融合联通主义学习和多层时序图Transformer的知识追踪模型

doi:10.11896/jsjkx.250700188

Abstract

Abstract: Knowledge Tracing(KT) aims to model learners’ knowledge states based on their historical exercise records and predict their future performance.Traditional KT research primarily focuses on modeling learners’ behavioral sequences while overlooking the topological structure among knowledge concepts.Although recent methods using static knowledge graphs have shown progress,they fail to adequately capture the dynamic graph-structured relationships among learners,questions,and knowledge concepts,thereby ignoring potential correlations in the knowledge acquisition process and limiting model generalizability and interpretability.To address these limitations,this paper proposes a Graph Transformer Knowledge Tracing(GTKT) model that integrates connectivism learning theory with a multi-layer temporal graph Transformer.Firstly,guided by connectivism learning theory,it constructs temporal learner subgraphs to represent historical exercise sequences,proposing a time-aware hierarchical subgraph sampling strategy and a neighbor co-occurrence encoder to discover latent node relationships.Secondly,based on lear-ning and forgetting theories,it designs a multi-band temporal encoder to capture temporal characteristics in learning behaviors and builds a multi-feature fusion module integrating learner-question-knowledge concepts interactions.Thirdly,it develops a multi-layer temporal graph Transformer module for dynamic knowledge state modeling and prediction.Experimental results on six public datasets demonstrate that GTKT outperforms mainstream knowledge tracing models in predicting learner performance.

Key words: Knowledge tracing, Connectivism learning, Educational theory, Graph Transformer, Multi-features fusion

CLC Number:

TP391.6

LI Jiahao, JING Junchang, XU Qian, LIU Dong. GTKT:Knowledge Tracing Model Integrating Connectivism Learning and Multi-layer TemporalGraph Transformer[J].Computer Science, 2026, 53(2): 78-88.

References

[1]YAN Q Y,SUN H,SI Y Q,et al.Multimodality and Forgetting Mechanisms Model for Knowledge Tracing[J].Computer Science,2024,51(7):133-139.
[2]XIE P Z,LI G J,LI T.Knowledge Tracing Model Based on Exercise-Knowledge Point Heterogeneous Graph and Multi-feature Fusion[J].Computer Science,2025,52(3):197-205.
[3]DONG Y F,HUANG G,XUE W R,et al.Graph Attention Deep Knowledge Tracing Model Integrated with IRT[J].Computer Science,2023,50(3):173-180.
[4]BAKER R S J D,CORBETT A T,ALEVEN V.More Accurate Student Modeling Through Contextual Estimation of Slip and Guess Probabilities in Bayesian Knowledge Tracing[C]//Proceedings of the 9th International Conference on Intelligent Tutoring Systems(ITS).2008:406-415.
[5]EMBRETSON S E,REISE S P.Item Response Theory[M].NewYork:Psychology Press,2013.
[6]PAVLIK P I,CEN H,KOEDINGER K R.Performance Factors Analysis - A New Alternative to Knowledge Tracing[C]//Proceedings of the 2009 Conference on Artificial Intelligence in Education(AIED).2009:531-538.
[7]PIECH C,BASSEN J,HUANG J,et al.Deep Knowledge Tra-cing[C]//Proceedings of the 28th Conference on Neural Information Processing Systems(NeurIPS).2015:505-513.
[8]LIPTON Z C,BERKOWITZ J,ELKAN C.A Critical Review of Recurrent Neural Networks for Sequence Learning[J].arXiv:1506.00019,2015.
[9]GHOSH A,HEFFERNAN N,LAN A S.Context－aware Attentive Knowledge Tracing[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining(KDD).ACM,2020:2330-2339.
[10]YIN Y,DAI L,HUANG Z Y,et al.Tracing Knowledge Instead of Patterns:Stable Knowledge Tracing with Diagnostic Transformer[C]//Proceedings of the 26th ACM Web Conference 2023(WWW).ACM,2023:855-864.
[11]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isAll You Need[C]//Proceedings of the 31st Conference on Neural Information Processing Systems(NeurIPS).ACM,2017:6000-6010.
[12]NAKAGAWA H,IWASAWA Y,MAEDA Y.Graph-basedKnowledge Tracing:Modeling Student Proficiency Using Graph Neural Network[C]//Proceedings of the 2019 IEEE/WIC/ACM International Conference on Web Intelligence(WI).2019:156-163.
[13]CUI C R,YAO Y M,ZHANG C Y,et al.DGEKT:A DualGraph Ensemble Learning Method for Knowledge Tracing[J].ACM Transactions on Information Systems,2024,42(3):1-24.
[14]YANG Y,SHEN J J,QU Y R,et al.GIKT:A Graph-based Interaction Model for Knowledge Tracing[C]//Proceedings of the 2020 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases(ECML-PKDD).2020:299-315.
[15]WU Z,HUANG L,HUANG Q,et al.SGKT:Session Graph-based Knowledge Tracing for Student Performance Prediction[J].Expert Systems with Applications,2022,206:117681.
[16]CHENG K,PENG L Z,WANG P Y,et al.DyGKT:DynamicGraph Learning for Knowledge Tracing[C]//Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining(KDD).ACM,2024:409-420.
[17]CHEN Z Y,SHAN Z L.Research Advances in Knowledge Tra-cing[J].Computer Science,2022,49(10):83-95.
[18]ANDERSON J R,BOYLE C F,CORBETT A T,et al.Cognitive Modeling and Intelligent Tutoring[M]//Artificial Intelligence.1990:7-49.
[19]PERKINS D N,SALOMON G.Transfer of Learning[J].International Encyclopedia of Education,1992,2:6452-6457.
[20]ROEDIGER H L,KARPICKE J D.Test-enhanced Learning:Taking Memory Tests Improves Long-term Retention[M]//Psychological Science.2006:249-255.
[21]MAYO R C M,RUSSELL S,NUGENT T,et al.Revealing the learning in learning curves[C]//Proceedings of the 16th International Conference on Artificial Intelligence in Education(AIED).2013:473-482.
[22]EBBINGHAUS H.Memory:A Contribution to ExperimentalPsychology[J].Annals of Neurosciences,2013,20(4):155-156.
[23]YELLE L E.The LearningCurve:Historical Review and Comprehensive Survey[J].Decision Sciences,1979,10(2):302-328.
[24]BRUNER J S.Toward a Theory of Instruction[M].Cambridge:Harvard University Press,1966.
[25]HOCHREITER S,SCHMIDHUBER J.Long Short-termMemory[J].Neural Computation,1997,9(8):1735-1780.
[26]YEUNG C K,YEUNG D Y.Addressing Two Problems in Deep Knowledge Tracing via Prediction-Consistent Regularization[C]//Proceedings of the 5th Annual ACM Conference on Learning at Scale(L@S).ACM,2018:1-10.
[27]PANDEY S,KARYPIS G.A Self Attentive Model for Know-ledge Tracing[C]//Proceedings of the 12th International Confe-rence on Educational Data Mining(EDM).2019:384-389.
[28]RASCH G.Probabilistic Models for Some Intelligence and At-tainment Tests[M].Chicago:University of Chicago Press,1993.
[29]LIU Z T,LIU Q Q,CHEN J H,et al.simpleKT:A Simple but Tough-to-Beat Baseline for Knowledge Tracing[C]//Procee-dings of the 11th International Conference on Learning Representations(ICLR).2023.
[30]SHEN S H,LIU Q,CHEN E H,et al.Learning Process-Consistent Knowledge Tracing[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining(KDD).ACM,2021:1452-1460.
[31]LIU Q,HUANG Z Y,YIN Y,et al.EKT:Exercise-awareKnowledge Tracing for Student Performance Prediction[J].IEEE Transactions on Knowledge and Data Engineering,2021,33(1):100-115.
[32]CHEN J H,LIU Z T,HUANG S Y,et al.Improving Interpre-tability of Deep Sequential Knowledge Tracing Models with Question-Centric Cognitive Representations[C]//Proceedings of the 37th AAAI Conference on Artificial Intelligence.AAAI,2023:14196-14204.
[33]CAI L Q,LIU Y C,REN B,et al.A personalized knowledge re-commendation method integrating muti-knowledge point and group characteristics[J].Journal of Chongqing University of Posts & Telecommunications(Natural Science Edition),2024,36(5):1023-1031.
[34]WU S,SUN F,ZHANG W,et al.Graph Neural Networks in Recommender Systems:A Survey[J].ACM Computing Surveys,2023,55(5):1-37.
[35]ALADE Y A,FENG C,MAO X,et al.Graph Neural Networks for Visual Question Answering:A Systematic Review[J].Multimedia Tools and Applications,2023,83:55471-55508.
[36]DAS N,SADHUKHAN B,CHATTERJEE R,et al.Integrating Sentiment Analysis with Graph Neural Networks for Enhanced Stock Prediction:A Comprehensive Survey[J].Decision Analy-tics Journal,2024,10:100417.
[37]LIN Y,RUAN T,LIU J,et al.A Survey on Neural Data-to-Text Generation[J].IEEE Transactions on Knowledge and Data Engineering,2024,36(4):1431-1449.
[38]LIU Y F,YANG Y,CHEN X Y,et al.Improving KnowledgeTracing via Pre-training Question Embeddings[C]//Procee-dings of the 29th International Joint Conference on Artificial Intelligence.2021:1577-1583.
[39]CHEN L,XU Y Q.The Philosophical Outlook of Connectivism and Its Enlightenment on Educational Reform[J].Education Research,2023,3(14):16-25.
[40]DOWNES S.Recent Work in Connectivism[J].European Journal of Open,Distance and E-Learning,2020,22(2):113-133.
[41]YU L,SUN L L,DU B W,et al.Towards Better Dynamic Graph Learning:New Architecture and Unified Library[C]//Procee-dings of the 36th Conference on Neural Information Processing Systems(NeurIPS).2023:67686-67700.
[42]LOSHCHILOV I,HUTTER F.Decoupled Weight Decay Regularization.[C]//Proceedings of the 7th International Conference on Learning Representations(ICLR).2019.
[43]KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[J].arXiv:1412.6980,2014.
[44]ADAM P,SAM G,FRANCISCO M,et al.Pytorch:An imperative style,high-performance deep learning library[C]//Procee-dings of the 33rd Conference on Neural Information Processing Systems(NeurIPS).2019:8026-8037.
[45]GHODAI A,WANG Q.Deep graph memory networks for forgetting-robust knowledge tracing[J].IEEE Transactions on Knowledge and Data Engineering,2022,35(8):7844-7855.
[46]SHEN X X,YU F H,LIU Y Q,et al.Revisiting KnowledgeTracing:A Simple and Powerful Model[C]//Proceedings of the 32nd ACM International Conference on Multimedia(MM).2024:263-272.

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