微信服务号

微信订阅号

2025年3月31日 18:06 星期一
首页 > 过刊浏览>2023年第34卷第3期 >1365-1395. DOI:10.13328/j.cnki.jos.006715
PDF HTML阅读 XML下载 导出引用 引用提醒
深度知识追踪模型综述和性能比较
作者:
作者简介:

王宇(1997-),男,博士生,主要研究领域为数据驱动的计算教育学;陆雪松(1985-),男,博士,副研究员,CCF专业会员,主要研究领域为数据驱动的计算教育学;朱梦霞(1996-),女,硕士,主要研究领域为数据驱动的计算教育学;周傲英(1965-),男,博士,教授,博士生导师,CCF会士,主要研究领域为数据库,数据管理,数字化转型,金融科技,计算教育,教育科技和物流科技等数据驱动的应用;杨尚辉(1996-),男,硕士,主要研究领域为图像处理,计算机视觉,数据驱动的计算教育学.

通讯作者:

陆雪松，xslu@dase.ecnu.edu.cn

基金项目:

国家自然科学基金（61977026，62072185）


Review and Performance Comparison of Deep Knowledge Tracing Models
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [80]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    知识追踪是一种重要的认知诊断方法，往往被用于在线学习平台、智能辅导系统等信息化教学平台中.知识追踪模型通过分析学生与课程作业的交互数据，即时模拟学生对课程知识点的掌握水平，模拟的结果可以用来预测学生未来的学习表现，并帮助他们规划个性化的学习路径.在过去20多年中，知识追踪模型的构建通常基于统计学和认知科学的相关理论.随着教育大数据的开放和应用，基于深度神经网络的模型（以下简称“深度知识追踪模型”）以其简单的理论基础和优越的预测性能，逐渐取代了传统模型，成为知识追踪领域新的研究热点.根据所使用的神经网络结构，阐述近年来代表性深度知识追踪模型的算法细节，并在5个公开数据集上对这些模型的性能进行全面比较.最后讨论了深度知识追踪的应用案例和若干未来研究方向.

    Abstract:

    Knowledge tracking is an important cognitive diagnosis method, which is often used in digitalized education platforms such as online learning platforms and intelligent tutoring systems. By analyzing students' interactions with course assignments, knowledge tracing models can simulate their mastery level of knowledge concepts in courses in real time. The simulation results can be used to predict students' future learning performance and help them plan personalized learning paths. In the past 20 years, knowledge tracing models have been constructed based on theories of statistics and cognitive science. With the openness and application of educational big data, models based on deep neural networks, referred to as "deep knowledge tracing models", have gradually replaced traditional models due to their simple theoretical foundations and superior predictive performances, and become a new research hotspot in the field of knowledge tracing. According to the neural network architectures, the algorithm details of recent representative deep models for knowledge tracing are illustrated, and a comprehensive performance evaluation of the models on five publicly available datasets is conducted. Finally, some use cases and future research directions of deep knowledge tracing are discussed.

    参考文献
    [1] Wolins L, Wright BD, Rasch G.Probabilistic models for some intelligence and attainment tests.Journal of the American Statistical Association, 1982, 77(377):220.
    [2] Corbett AT, Anderson JR.Knowledge tracing:Modeling the acquisition of procedural knowledge.User Modelling and User-adapted Interaction, 1995, 4(4):253-278.
    [3] Cen H, Koedinger K, Junker B.Learning factors analysis-A general method for cognitive model evaluation and improvement.In:Ikeda M, Ashley KD, Chan TW, eds.Proc.of the Intelligent Tutoring Systems, Vol.4053.Berlin, Heidelberg:Springer, 2006.164-175.
    [4] Pavlik PI, Cen H, Koedinger KR.Performance factors analysis-A new alternative to knowledge tracing.In:Proc.of the Conf.on Artificial Intelligence in Education:Building Learning Systems that Care:From Knowledge Representation to Affective Modelling.2009.531-538.
    [5] Chi M, Koedinger K, Gordon G, Jordan P, VanLehn K.Instructional factors analysis:A cognitive model for multiple instructional interventions.In:Proc.of the 12th Int'l Conf.on Educational Data Mining.2011.61-70.
    [6] Liu HY, Zhang TC, Wu PW, Yu G.A review of knowledge tracking.Journal of East China Normal University (Natural Science), 2019, 2019(5):1-15(in Chinese with English abstract).[doi:10.3969/j.issn.1000-5641.2019.05.001]
    [7] Liu Q, Shen S, Huang Z, Chen E, Zheng Y.A survey of knowledge tracing.arXiv:2105.15106, 2021.
    [8] Piech C, Spencer J, Huang J, Ganguli S, Sahami M, Guibas L, Sohl-Dickstein J.Deep knowledge tracing.In:Proc.of the Advances in Neural Information Processing Systems.Montreal, 2015.505-513.
    [9] Zhang J, Shi X, King I, Yeung DY.Dynamic key-value memory networks for knowledge tracing.In:Proc.of the 26th Int'l Conf.on World Wide Web.2017.765-774.
    [10] Weston J, Chopra S, Bordes A.Memory networks.arXiv:1410.3916, 2015.
    [11] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser Ł, Polosukhin I.Attention is all you need.In:Proc.of the Advances in Neural Information Processing Systems.Curran Associates, Inc., 2017.5998-6008.
    [12] Pandey S, Karypis G.A self-attentive model for knowledge tracing.In:Proc.of the 12th Int'l Conf.on Educational Data Mining.2019.384-389.
    [13] Sha L, Hong P.Neural knowledge tracing.In:Proc.of the Int'l Conf.on Brain Function Assessment in Learning.Cham:Springer, 2017.108-117.
    [14] Liu Q, Huang Z, Yin Y, Chen E, Xiong H, Su Y, Hu G.EKT:Exercise-aware knowledge tracing for student performance prediction.IEEE Trans.on Knowledge and Data Engineering, 2019, 33(1):100-115.
    [15] Lee J, Yeung DY.Knowledge query network for knowledge tracing:How knowledge interacts with skills.In:Proc.of the 9th Int'l Conf.on Learning Analytics & Knowledge.Tempe:ACM, 2019.491-500.
    [16] Yang H, Cheung LP.Implicit heterogeneous features embedding in deep knowledge tracing.Cognitive Computation, 2018, 10(1):3-14.
    [17] Zhang L, Xiong X, Zhao S, Botelho A, Heffernan NT.Incorporating rich features into deep knowledge tracing.In:Proc.of the 4th (2017) ACM Conf.on Learning@Scale.Cambridge, Massachusetts:ACM, 2017.169-172.
    [18] Su Y, Liu Q, Liu Q, Huang Z, Yin Y, Chen E, Ding C, Wei S, Hu G.Exercise-enhanced sequential modeling for student performance prediction.In:Proc.of the AAAI Conf.on Artificial Intelligence.2018.2435-2443.
    [19] Wang Z, Feng X, Tang J, Huang GY, Liu Z.Deep knowledge tracing with side information.In:Proc.of the Int'l Conf.on Artificial Intelligence in Education.Cham:Springer, 2019.303-308.
    [20] Sonkar S, Waters AE, Lan AS, Grimaldi PJ, Baraniuk RG.QDKT:Question-centric deep knowledge tracing.In:Proc.of the Int'l Conf.on Artificial Intelligence in Education.Cham:Springer, 2021.433-437.
    [21] Liu D, Zhang Y, Zhang J, Li Q, Zhang C, Yin Y.Multiple features fusion attention mechanism enhanced deep knowledge tracing for student performance prediction.IEEE Access, 2020, 8:194894-194903.
    [22] Tong H, Zhou Y, Wang Z.Exercise hierarchical feature enhanced knowledge tracing.In:Proc.of the Int'l Conf.on Artificial Intelligence in Education.Cham:Springer, 2020.324-328.
    [23] Zhang N, Du Y, Deng K, Li L, Shen J, Sun G.Attention-based knowledge tracing with heterogeneous information network embedding.In:Li G, Shen HT, Yuan Y, Wang X, Liu H, Zhao X, eds.Proc.of the Knowledge Science, Engineering and Management, Vol.12274.Cham:Springer, 2020.95-103.
    [24] Yang Y, Shen J, Qu Y, Liu Y, Wang K, Zhu Y, Zhang W, Yu Y.GIKT:A graph-based interaction model for knowledge tracing.In:Proc.of the Joint European Conf.on Machine Learning and Knowledge Discovery in Databases.Cham:Springer, 2020.299-315.
    [25] Chen P, Lu Y, Zheng VW, Pian Y.Prerequisite-driven deep knowledge tracing.In:Proc.of the IEEE Int'l Conf.on Data Mining (ICDM).Singapore:IEEE, 2018.39-48.
    [26] Minn S, Yu Y, Desmarais MC, Zhu F, Vie JJ.Deep knowledge tracing and dynamic student classification for knowledge tracing.In:Proc.of the IEEE Int'l Conf.on Data Mining (ICDM).Qingdao:IEEE, 2018.1182-1187.
    [27] Nagatani K, Zhang Q, Sato M, Chen YY, Chen F, Ohkuma T.Augmenting knowledge tracing by considering forgetting behavior.In:Proc.of the World Wide Web Conf.San Francisco:ACM, 2019.3101-3107.
    [28] Ebbinghaus H.Memory:A contribution to experimental psychology.Annals of Neurosciences, 2013, 20(4):155-156.
    [29] Huang Z, Liu Q, Chen Y, Wu L, Xiao K, Chen E, Ma H, Hu G.Learning or forgetting? A dynamic approach for tracking the knowledge proficiency of students.ACM Trans.on Information Systems (TOIS), 2020, 38(2):1-33.
    [30] Long T, Liu Y, Shen J, Zhang W, Yu Y.Tracing knowledge state with individual cognition and acquisition estimation.In:Proc.of the 44th Int'l ACM SIGIR Conf.on Research and Development in Information Retrieval.Virtual Event:ACM, 2021.173-182.
    [31] Abdelrahman G, Wang Q.Knowledge tracing with sequential key-value memory networks.In:Proc.of the 42nd Int'l ACM SIGIR Conf.on Research and Development in Information Retrieval.2019.175-184.
    [32] Chaudhry R, Singh H, Dogga P, Saini SK.Modeling Hint-taking Behavior and Knowledge State of Students with Multi-task Learning.Int'l Educational Data Mining Society, 2018.
    [33] Sun X, Zhao X, Ma Y, Yuan X, He F, Feng J.Muti-behavior features based knowledge tracking using decision tree improved DKVMN.In:Proc.of the ACM Turing Celebration Conf.Chengdu:ACM, 2019.1-6.
    [34] Minn S, Desmarais MC, Zhu F, Xiao J, Wang J.Dynamic student classiffication on memory networks for knowledge tracing.In:Proc.of the Pacific-Asia Conf.on Knowledge Discovery and Data Mining.Cham:Springer, 2019.163-174.
    [35] Sun X, Zhao X, Li B, Ma Y, Sutcliffe R, Feng J.Dynamic key-value memory networks with rich features for knowledge tracing.IEEE Trans.on Cybernetics, 2022, 52(8):8239-8245.
    [36] Ha H, Hwang U, Hong Y, Jang J, Yoon S.Deep trustworthy knowledge tracing.arXiv:1805.10768, 2018.
    [37] Yeung CK.Deep-IRT:Make deep learning based knowledge tracing explainable using item response theory.arXiv:1904.11738, 2019.
    [38] Gan W, Sun Y, Sun Y.Knowledge interaction enhanced knowledge tracing for learner performance prediction.In:Proc.of the 7th Int'l Conf.on Behavioural and Social Computing (BESC).Bournemouth:IEEE, 2020.1-6.
    [39] Abdelrahman G, Wang Q.Deep graph memory networks for forgetting-robust knowledge tracing.arXiv:2108.08105, 2021.
    [40] Choi Y, Lee Y, Cho J, Baek J, Kim B, Cha Y, Shin D, Bae C, Heo J.Towards an appropriate query, key, and value computation for knowledge tracing.In:Proc.of the 11th Int'l Learning Analytics and Knowledge Conf.(LAK 2021).2021.490-496.
    [41] Bhatt S, Zhao J, Thille C, Zimmaro D, Gattani N.A novel approach for knowledge state representation and prediction.In:Proc.of the 7th ACM Conf.on Learning@Scale.Virtual Event:ACM, 2020.353-356.
    [42] Zhang C, Jiang Y, Zhang W, Gu C.MUSE:Multi-scale temporal features evolution for knowledge tracing.arXiv:2102.00228, 2021.
    [43] Oya T, Morishima S.LSTM-SAKT:LSTM-encoded SAKT-like transformer for knowledge tracing.arXiv:2102.00845, 2021.
    [44] Ghosh A, Heffernan N, Lan AS.Context-aware attentive knowledge tracing.In:Proc.of the 26th ACM SIGKDD Int'l Conf.on Knowledge Discovery & Data Mining.2020.2330-2339.
    [45] Pandey S, Srivastava J.RKT:Relation-aware self-attention for knowledge tracing.In:Proc.of the 29th ACM Int'l Conf.on Information & Knowledge Management.2020.1205-1214.
    [46] Zhou Y, Li X, Cao Y, Zhao X, Ye Q, Lv J.LANA:Towards Personalized Deep Knowledge Tracing through Distinguishable Interactive Sequences.Int'l Educational Data Mining Society, 2021.
    [47] Shen S, Liu Q, Chen E, Wu H, Huang Z, Zhao W, Su Y, Ma H, Wang S.Convolutional knowledge tracing:modeling individualization in student learning process.In:Proc.of the 43rd Int'l ACM SIGIR Conf.on Research and Development in Information Retrieval.Virtual Event:ACM, 2020.1857-1860.
    [48] Nakagawa H, Iwasawa Y, Matsuo Y.Graph-based knowledge tracing:modeling student proficiency using graph neural network.In:Proc.of the IEEE/WIC/ACM Int'l Conf.on Web Intelligence.Thessaloniki:ACM, 2019.156-163.
    [49] Cheng S, Liu Q, Chen E, Zhang K, Huang Z, Yin Y, Huang X, Su Y.AdaptKT:A domain adaptable method for knowledge tracing.In:Proc.of the 15th ACM Int'l Conf.on Web Search and Data Mining.Virtual Event:ACM, 2022.123-131.
    [50] Yeung CK, Yeung DY.Incorporating features learned by an enhanced deep knowledge tracing model for STEM/non-STEM job prediction.Int'l Journal of Artificial Intelligence in Education, 2019, 29(3):317-341.
    [51] Swamy V, Guo A, Lau S, Wu W, Wu M, Pardos Z, Culler D.Deep knowledge tracing for free-form student code progression.In:Penstein Rosé C, Martínez-Maldonado R, Hoppe HU, Luckin R, Mavrikis M, Porayska-Pomsta K, McLaren B, Du Boulay B, eds.Proc.of the Artificial Intelligence in Education, Vol.10948.Cham:Springer, 2018.348-352.
    [52] Wang L, Sy A, Liu L, Piech C.Deep knowledge tracing on programming exercises.In:Proc.of the 4th (2017) ACM Conf.on Learning@Scale.Cambridge Massachusetts:ACM, 2017.201-204.
    [53] Kaneko M, Kajiwara T, Komachi M.TMU system for SLAM-2018.In:Proc.of the 13th Workshop on Innovative Use of NLP for Building Educational Applications.New Orleans:Association for Computational Linguistics, 2018.365-369.
    [54] Jiang W, Pardos ZA, Wei Q.Goal-based course recommendation.In:Proc.of the 9th Int'l Conf.on Learning Analytics & Knowledge.Tempe:ACM, 2019.36-45.
    [55] Zhang J, King I.Topological order discovery via deep knowledge tracing.In:Hirose A, Ozawa S, Doya K, Ikeda K, Lee M, Liu D, eds.Proc.of the Neural Information Processing, Vol.9950.Cham:Springer, 2016.112-119.
    [56] Zeiler MD, Fergus R.Visualizing and understanding convolutional networks.In:Proc.of the European Conf.on Computer Vision.Cham:Springer, 2014.818-833.
    [57] Nguyen A, Dosovitskiy A, Yosinski J, Brox T, Clune J.Synthesizing the preferred inputs for neurons in neural networks via deep generator networks.In:Proc.of the Advances in Neural Information Processing Systems, Vol.29.2016.3387-3395.
    [58] Baehrens D, Schroeter T, Harmeling S, Kawanabe M, Hansen K.How to explain individual classification decisions.The Journal of Machine Learning Research, 2010, 11(6):1803-1831.
    [59] Sundararajan M, Taly A, Yan Q.Axiomatic attribution for deep networks.In:Proc.of the Int'l Conf.on Machine Learning.PMLR, 2017.3319-3328.
    [60] Bastings J, Filippova K.The elephant in the interpretability room:Why use attention as explanation when we have saliency methods? In:Proc.of the 3rd BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP.2020.149-155.
    [61] Li J, Chen X, Hovy E, Jurafsky D.Visualizing and understanding neural models in NLP.In:Proc.of the NAACL-HLT.2016.681-691.
    [62] Bach S, Binder A, Montavon G, Klauschen F, Müller KR, Samek W.On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation.PLOS ONE, 2015, 10(7):Article No.0130140.
    [63] Arras L, Montavon G, Müller KR, Samek W.Explaining recurrent neural network predictions in sentiment analysis.In:Proc.of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis.2017.159-168.
    [64] Li J, Monroe W, Jurafsky D.Understanding neural networks through representation erasure.arXiv:1612.08220, 2016.
    [65] DeYoung J, Jain S, Rajani NF, Lehman E, Xiong C, Socher R, Wallace BC.ERASER:A benchmark to evaluate rationalized NLP models.In:Proc.of the 58th Annual Meeting of the Association for Computational Linguistics.2020.4443-4458.
    [66] Jiang B, Wu S, Yin C, Zhang H.Knowledge tracing within single programming practice using problem-solving process data.IEEE Trans.on Learning Technologies, 2020, 13(4):822-832.
    [67] Renduchintala A, Koehn P, Eisner J.Knowledge tracing in sequential learning of inflected vocabulary.In:Proc.of the 21st Conf.on Computational Natural Language Learning (CoNLL 2017).Vancouver:Association for Computational Linguistics, 2017.238-247.
    [68] Zylich B, Lan A.Linguistic skill modeling for second language acquisition.In:Proc.of the LAK21:the 11th Int'l Learning Analytics and Knowledge Conf.Irvine:ACM, 2021.141-150.
    [69] Allamanis M, Brockschmidt M, Khademi M.Learning to represent programs with graphs.arXiv:1711.00740, 2017.
    [70] Zhang J, Wang X, Zhang H, Sun H, Wang K, Liu X.A novel neural source code representation based on abstract syntax tree.In:Proc.of the IEEE/ACM 41st Int'l Conf.on Software Engineering (ICSE).Montreal:IEEE, 2019.783-794.
    [71] Piech C, Huang J, Nguyen A, Phulsuksombati M, Sahami M, Guibas L.Learning program embeddings to propagate feedback on student code.In:Proc.of the Int'l Conf.on Machine Learning.PMLR, 2015.1093-1102.
    [72] Wei H, Li H, Xia M, Wang Y, Qu H.Predicting student performance in interactive online question pools using mouse interaction features.In:Proc.of the 10th Int'l Conf.on Learning Analytics & Knowledge.2020.645-654.
    [73] Chopade P, Khan SM, Edwards D, Von Davier A.Machine learning for efficient assessment and prediction of human performance in collaborative learning environments.In:Proc.of the IEEE Int'l Symp.on Technologies for Homeland Security (HST).Woburn:IEEE, 2018.1-6.
    [74] Olsen JK, Sharma K, Rummel N, Aleven V.Temporal analysis of multimodal data to predict collaborative learning outcomes.British Journal of Educational Technology, 2020, 51(5):1527-1547.
    [75] He X, Liao L, Zhang H, Nie L, Hu X, Chua TS.Neural collaborative filtering.In:Proc.of the 26th Int'l Conf.on World Wide Web.2017.173-182.
    [76] Zhao J, Bhatt S, Thille C, Zimmaro D, Gattani N.Interpretable personalized knowledge tracing and next learning activity recommendation.In:Proc.of the 7th ACM Conf.on Learning@Scale.Virtual Event:ACM, 2020.325-328.
    [77] Huang Z, Liu Q, Zhai C, Yin Y, Chen E, Gao W, Hu G.Exploring multi-objective exercise recommendations in online education systems.In:Proc.of the 28th ACM Int'l Conf.on Information and Knowledge Management.Beijing:ACM, 2019.1261-1270.
    [78] Bassen J, Balaji B, Schaarschmidt M, Thille C, Painter J, Zimmaro D, Games A, Fast E, Mitchell JC.Reinforcement learning for the adaptive scheduling of educational activities.In:Proc.of the CHI Conf.on Human Factors in Computing Systems.Honolulu:ACM, 2020.1-12.
    附中文参考文献
    [6] 刘恒宇, 张天成, 武培文, 于戈.知识追踪综述.华东师范大学学报(自然科学版), 2019, 2019(5):1-5.[doi:10.3969/j.issn.1000-5641.2019.05.001]
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

王宇,朱梦霞,杨尚辉,陆雪松,周傲英.深度知识追踪模型综述和性能比较.软件学报,2023,34(3):1365-1395

复制
分享
文章指标
  • 点击次数:2308
  • 下载次数: 4300
  • HTML阅读次数: 2808
  • 引用次数: 0
历史
  • 收稿日期:2021-07-18
  • 最后修改日期:2022-05-07
  • 在线发布日期: 2022-07-22
  • 出版日期: 2023-03-06
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第19758716位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号