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
CHEN Cheng1, DONG Yongquan1,2,3* , JIA Rui1, LIU Yuan1
CLC Number:
[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. |
[1] | Jiachun LI,Bowen LI,Jianbo CHANG. An efficient and lightweight RGB frame-level face anti-spoofing model [J]. Journal of Shandong University(Engineering Science), 2023, 53(6): 1-7. |
[2] | Yujiang FAN,Huanhuan HUANG,Jiaxiong DING,Kai LIAO,Binshan YU. Resilience evaluation system of the old community based on cloud model [J]. Journal of Shandong University(Engineering Science), 2023, 53(5): 1-9, 19. |
[3] | Ying LI,Jiankun WANG. The classification of mild cognitive impairment based on supervised graph regularization and information fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(4): 65-73. |
[4] | LIU Xing, YANG Lu, HAO Fanchang. Finger vein image retrieval based on multi-feature fusion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 118-126. |
[5] | YU Yixuan, YANG Geng, GENG Hua. Multimodal hierarchical keyframe extraction method for continuous combined motion [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 42-50. |
[6] | ZHANG Hao, LI Ziling, LIU Tong, ZHANG Dawei, TAO Jianhua. A technology prediction model based on fuzzy Bayesian networks with sociological factors [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 23-33. |
[7] | WU Yanli, LIU Shuwei, HE Dongxiao, WANG Xiaobao, JIN Di. Poisson-gamma topic model of describing multiple underlying relationships [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 51-60. |
[8] | YU Mingjun, DIAO Hongjun, LING Xinghong. Online multi-object tracking method based on trajectory mask [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 61-69. |
[9] | HUANG Huajuan, CHENG Qian, WEI Xiuxi, YU Chuchu. Adaptive crow search algorithm with Jaya algorithm and Gaussian mutation [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 11-22. |
[10] | LIU Fangxu, WANG Jian, WEI Benzheng. Auxiliary diagnosis algorithm for pediatric pneumonia based on multi-spatial attention [J]. Journal of Shandong University(Engineering Science), 2023, 53(2): 135-142. |
[11] | Yue YUAN,Yanli WANG,Kan LIU. Named entity recognition model based on dilated convolutional block architecture [J]. Journal of Shandong University(Engineering Science), 2022, 52(6): 105-114. |
[12] | Xiaobin XU,Qi WANG,Bin GAO,Zhiyu SUN,Zhongjun LIANG,Shangguang WANG. Pre-allocation of resources based on trajectory prediction in heterogeneous networks [J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 12-19. |
[13] | Yinfeng MENG,Qingfang LI. Recognition learning based on multivariate functional principal component representation [J]. Journal of Shandong University(Engineering Science), 2022, 52(3): 1-8. |
[14] | Xiushan NIE,Yuling MA,Huiyan QIAO,Jie GUO,Chaoran CUI,Zhiyun YU,Xingbo LIU,Yilong YIN. Survey on student academic performance prediction from the perspective of task granularity [J]. Journal of Shandong University(Engineering Science), 2022, 52(2): 1-14. |
[15] | Tongyu JIANG, Fan CHEN, Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8. |
|