Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (11): 241-247.doi: 10.11896/jsjkx.221100169

• Artificial Intelligence • Previous Articles     Next Articles

Attention Based Concept Enhanced Cognitive Diagnosis

YUAN Dongxue, SUN Quansen, FU Peng   

  1. School of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210000,China
  • Received:2022-11-21 Revised:2023-03-10 Online:2023-11-15 Published:2023-11-06
  • About author:YUAN Dongxue,born in 1996,postgra-duate.Her main research interests include educational data mining and so on.SUN Quansen,born in 1963,Ph.D,professor,Ph.D supervisor,is a member of China Computer Federation.His main research interests include image recognition and computer vision.

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Abstract: Cognitive diagnosis is a fundamental problem in intelligent education systems,which aims to evaluate the mastery le-vels of students on different knowledge concepts.Although the performance current deep learning-based cognitive diagnostic me-thods has improved greatly compared with traditional methods,they cannot fully exploit the potential correlation between concepts.To this end,this paper proposes an attention-based concept enhanced cognitive diagnosis(ACECD) model to obtain more accurate cognitive diagnostic results by modeling the relationship between related concepts.Specifically,we first project students,exercises,and concepts to factor vectors to perform complex interactions,and then feed the concept factors into a self-attention network to capture the implicit correlations that exist between concepts,and concept factor vector can be enhanced with the captured implicit relation.Finally,the enhanced concept factors are interacted with the student factor and the practice factor,and the interacted results are input into the diagnosis module to get the final diagnosis result.In addition,we also use the interaction between the practice factor and the concept factor to correct the bias of the manually-labeled Q matrix.The proposed model is compared with other methods on two real-world datasets,and the experimental results show that the ACECD model effectively improves the diagnostic results.

Key words: Attention, Cognitive diagnosis, Neural network

CLC Number: 

  • TP391.4
[1]CARBONELL J R.AI in CAI:An artificial-intelligence approachto computer-assisted instruction[J].IEEE Transactions on Man Machine Systems,1971,11(4):190-202.
[2] LIU Q,WU R,CHEN E,et al.Fuzzy cognitive diagnosis for modelling examinee performance[J].ACM Transactions on Intelligent Systems and Technology,2018,9(4):1-26.
[3]TANG X,CHEN Y,LI X,et al.A reinforcement learning approach to personalized learning recommendation systems[J].British Journal of Mathematical and Statistical Psychology,2019,72(1):108-135.
[4]ZHOU Y,HUANG C,HU Q,et al.Personalized learning full-path recommendation model based on LSTM neural networks[J].Information Sciences,2018,444:135-152.
[5] LIU Q,TONG S,LIU C,et al.Exploiting cognitive structure for adaptive learning[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2019:627-635.
[6]ELLIOTT M,PINAR W F,REYNOLDS W M,et al.Understanding curriculum:An introduction to the study of historical and contemporary curriculum discourses[J].Brock Education A Journal of Educational Research and Exercise,2010,13(1):100.
[7]EMBRETSON S E,REISE S P.Item response theory[M].Psychology Press,2013.
[8]ACKERMAN T A,GIERL M J,WALKER C M.Using multidi-mensional item response theory to evaluate educational and psychological tests[J].Educational Measurement:Issues and Practice,2003,22(3):37-51.
[9]RECKASE M D.Multidimensional item response theory models[M].New York:Springer, 2009:79-112.
[10]DE LA TORRE J.DINA model and parameter estimation:A didactic[J].Journal of Educational and Behavioral Statistics,2009,34(1):115-130.
[11] KOREN Y,BELL R,VOLINSKY C.Matrix factorization techniques for recommender systems[J].Computer,2009,42(8):30-37.
[12]WANG F,LIU Q,CHEN E,et al.Neural cognitive diagnosis for intelligent education systems[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020:6153-6161.
[13]ELLIS H C.The transfer of learning[M].Macmillan,1965.
[14]WOODWORTH R S,THORNDIKE E L.The influence of improvement in one mental function upon the efficiency of other functions[J].Psychological Review,1901,8(3):247.
[15]VON DAVIER M.The DINA model as a constrained generaldiagnostic model:Two variants of a model equivalency[J].British Journal of Mathematical and Statistical Psychology,2014,67(1):49-71.
[16] LAWRENCE S,GILES C L,TSOI A C,et al.Face recognition:A convolutional neural-network approach[J].IEEE Transactions on Neural Networks,1997,8(1):98-113.
[17]CHAN W,JAITLY N,LE Q,et al.Listen,attend and spell:A neural network for large vocabulary conversational speech re-cognition[C]//2016 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).IEEE,2016:4960-4964.
[18]BOJARSKI M,DEL TESTA D,DWORAKOWSKI D,et al.End to end learning for self-driving cars[J].arXiv:1604.07316,2016.
[19]PIECH C,BASSEN J,HUANG J,et al.Deep knowledge tracing[J].Advances in Neural Information Processing Systems,2015,28:1-9.
[20]WILLIAMS R J,ZIPSER D.A learning algorithm for continually running fully recurrent neural networks[J].Neural Computation,1998,1(2):270-280.
[21] TSUTSUMI E,KINOSHITA R,UENO M.Deep-IRT with independent student and item networks[J].International Educational Data Mining Society,2021:510-517.
[22]TONG S,LIU J,HONG Y,et al.Incremental cognitive diagnosis for intelligent education[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining.2022:1760-1770.
[23] HUANG J,LIU Q,WANG F,et al.Group-level cognitive diag-nosis:A multi-task learning perspective[C]//2021 IEEE International Conference on Data Mining.IEEE,2021:210-219.
[24]ZHOU Y,LIU Q,WU J,et al.Modeling context-aware features for cognitive diagnosis in student learning[C]//Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining.2021:2420-2428.
[25]LAROCHELLE H,HINTON G E.Learning to combine foveal glimpses with a third-order boltzmannmachine[J].Advances in Neural Information Processing Systems,2010,23:1-9.
[26] MNIH V,HEESS N,GRAVES A.Recurrent models of visualattention[J].Advances in Neural Information Processing Systems,2014,27:1-9.
[27]BAHDANAU D,CHO K,BENGIO Y.Neural machine translation by jointly learning to align and translate[C]//International Conference on Learning Representations.2014.
[28] XU K,BA J,KIROS R,et al.Show,attend,and tell:Neuralimage caption generation with visual attention[C]//InternationalConference on Machine Learning.PMLR,2015:2048-2057.
[29] YANG Z,YANG D,DYER C,et al.Hierarchical attention networks for document classification[C]//Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies.2016:1480-1489.
[30]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[J].Advances in Neural Information Processing Systems,2017,30:1-11.
[31]DEVLIN J,CHANG M W,LEE K,et al.BERT:Pre-training of deep bidirectional transformers for language understanding[C]//Proceedings of NAACL-HLT.2019:4171-4186.
[32] FENG M,HEFFERNAN N,KOEDINGER K.Addressing theassessment challenge with an online system that tutors as it assesses[J].User Modeling and User-Adapted Interaction,2009,19(3):243-266.
[33] PANDEY S,SRIVASTAVA J.RKT:Relation-aware self-attention for knowledge tracing[C]//Proceedings of the 29th ACM International Conference on Information & Knowledge Management.2020:1205-1214.
[34]PEI H,YANG B,LIU J,et al.Group sparse bayesian learning for active surveillance on epidemic dynamics[C]//Thirty-Second AAAI Conference on Artificial Intelligence.2017.
[35]BRADLEY A P.The use of the area under the ROC curve in the evaluation of machine learning algorithms[J].Pattern Recognition,1997,30(7):1145-1159.
[36] GLOROT X,BENGIO Y.Understanding the difficulty of trai-ning deep feedforward neural networks[C]//Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics.2010:249-256.
[37] KINGMA D,BA J.Adam:A method for stochastic optimization[J].arXiv,1412.6980,2014.
[38]FOUSS F,PIROTTE A,RENDERS J M,et al.Random-walkcomputation of similarities between nodes of a graph with application to collaborative recommendation[J].IEEE Transactions on Knowledge and Data Engineering,2007,19(3):355-369.
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