Journal of Shandong University(Engineering Science) ›› 2024, Vol. 54 ›› Issue (1): 100-108.doi: 10.6040/j.issn.1672-3961.0.2023.143

• Machine Learning & Data Mining • Previous Articles     Next Articles

Interpretable knowledge tracing based on the feature relevance of interaction sequence

CHEN Cheng1, DONG Yongquan1,2,3* , JIA Rui1, LIU Yuan1   

  1. 1. School of Computer Science and Technology, Jiangsu Normal University, Xuzhou 221116, Jiangsu, China;
    2. Jiangsu Educational Informatization Engineering Technology Research Center, Xuzhou 221116, Jiangsu, China;
    3. Xuzhou Cloud Computing Engineering Technology Research Center, Xuzhou 221116, Jiangsu, China
  • Published:2024-02-01

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136

CLC Number: 

  • TP391
[1] LIU Tieyuan, CHEN Wei, CHANG Liang, et al. Research advances in the knowledge tracing based on deep learning[J]. Journal of Computer Research and Development, 2022, 59(1): 81-104.
[2] CORBETT A T, ANDERSON J R. Knowledge tracing: modeling the acquisition of procedural knowledge[J]. User Modeling and User-Adapted Interaction, 1994, 4(4): 253-278.
[3] KÄSER T, KLINGLER S, SCHWING A G, et al. Dynamic bayesian networks for student modeling[J]. IEEE Transactions on Learning Technologies, 2017, 10(4): 450-462.
[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] //Intelligent Tutoring Systems. Berlin, Germany: Springer, 2008: 406-415.
[5] PARDOS Z A, HEFFERNAN N T. KT-idem: introducing item difficulty to the knowledge tracing model[C] //User Modeling, Adaption and Personalization. Berlin, Germany: Springer, 2011: 243-254.
[6] PIECH C, BASSEN J, HUANG J, et al. Deep knowledge tracing[J]. Advances in Neural Information Processing Systems, 2015, 28: 505-513.
[7] MINN S, YU Y, DESMARAIS M C, et al. Deep knowledge tracing and dynamic student classification for knowledge tracing[C] //2018 IEEE International Conference on Data Mining(ICDM). Singapore: IEEE, 2018: 1182-1187.
[8] YEUNG Chun-Kit, YEUNG Dit-Yan. Addressing two problems in deep knowledge tracing via prediction-consistent regularization[C] //Proceedings of the Fifth Annual ACM Conference on Learning at Scale. London, United Kingdom: Association for Computing Machinery, 2018: 1-10.
[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. New York, USA: Association for Computing Machinery, 2020: 2330-2339.
[10] PANDEY S, KARYPIS G. A self-attentive model for knowledge tracing[EB/OL].(2019-07-16)[2022-02-21]. http://arxiv.org/abs/1907.06837.
[11] CHOI Y, LEE Y, CHO J, et al. Towards an appropriate query, key, and value computation for knowledge tracing[C] //Proceedings of the Seventh ACM Conference on Learning @ Scale. New York, USA: Association for Computing Machinery, 2020: 341-344.
[12] ZHOU Y, LI X, CAO Y, et al. LANA: towards personalized deep knowledge tracing through distinguishable interactive sequences[EB/OL].(2021-04-20)[2023-03-20]. http://arxiv.org/abs/2105.06266.
[13] 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.
[14] LIU S, YU J, LI Q, et al. Ability boosted knowledge tracing[J]. Information Sciences, 2022, 596: 567-587.
[15] SIMONYAN K, VEDALDI A, ZISSERMAN A. Deep inside convolutional networks: visualising image classification models and saliency maps[EB/OL].(2014-04-19)[2023-02-13]. http://arxiv.org/abs/1312.6034.
[16] LI J, MONROE W, JURAFSKY D. Understanding neural networks through representation erasure[EB/OL].(2017-01-09)[2023-02-13]. http://arxiv.org/abs/1506.06579.
[17] FONG R C, VEDALDI A. Interpretable explanations of black boxes by meaningful perturbation[C] //2017 IEEE International Conference on Computer Vision(ICCV). New York, USA: [S.l.] , 2017: 3429-3437.
[18] KOH P W, LIANG P. Understanding black-box predictions via influence functions[C] //International Conference on Machine Learning. Sydney, Australia: PMLR, 2017: 1885-1894.
[19] ZHANG H, XIE Y, ZHENG L, et al. Interpreting multivariate shapley interactions in dnns[C] //Proceedings of the AAAI Conference on Artificial Intelligence. Menlo Park, USA: AAAI Press, 2021: 10877-10886.
[20] WICH M, MOSCA E, GORNIAK A, et al. Explainable abusive language classification leveraging user and network data[C] //Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Germany: Springer, 2021: 481-496.
[21] MA H, ZHANG H, ZHOU F, et al. Quantification and analysis of layer-wise and pixel-wise information discarding[C] //Proceedings of the 39th International Conference on Machine Learning. Sydney, Australia: PMLR, 2022: 14664-14698.
[22] LU Y, WANG D, MENG Q, et al. Towards interpretable deep learning models for knowledge tracing[C] //Artificial Intelligence in Education. Berlin, Germany: Springer, 2020: 185-190.
[23] BACH S, BINDER A, MONTAVON G, et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation[J]. PLOS ONE, 2015, 10(7): 0130140.
[24] DING X, LARSON E C. Why deep knowledge tracing has less depth than anticipated[C] //Proceedings of the 12th International Conference on Educational Data Mining. Montreal, Canada: ERIC, 2019: 282-287.
[25] DING X, LARSON E C. Incorporating uncertainties in student response modeling by loss function regularization[J]. Neurocomputing, 2020, 409: 74-82.
[26] HU Q, RANGWALA H. Reliable deep grade prediction with uncertainty estimation[C] //Proceedings of the 9th International Conference on Learning Analytics & Knowledge. New York, USA: Association for Computing Machinery, 2019: 76-85.
[27] GUHA R, KHAN A H, SINGH P K, et al. CGA: a new feature selection model for visual human action recognition[J]. Neural Computing and Applications, 2021, 33(10): 5267-5286.
[28] GHORBANI A, ZOU J Y. Neuron shapley: discovering the responsible neurons[J]. Advances in Neural Information Processing Systems, 2020, 33: 5922-5932.
[29] GABRIELLA C, GRILLI L, LIMONE P, et al. Deep learning for knowledge tracing in learning analytics: an overview[C] //CEUR Workshop Proceedings. Foggia, Italy: CEUR-WS, 2021: 1-10.
[30] ROZEMBERCZKI B, WATSON L, BAYER P, et al. The shapley value in machine learning[EB/OL].(2022-05-26)[2023-03-20]. http://arxiv.org/abs/2202.05594.
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