Review of Knowledge Tracing Preprocessing Based on Deep Learning

doi:10.3778/j.issn.1002-8331.2106-0552

Abstract

Abstract:

As education informatization keep deepening, knowledge tracing with the goal of predicting students’ knowledge status is becoming an important and challenging task in individualized education. As a time sequence task of educational data mining, knowledge tracing combines with the powerful feature extraction and modeling capabilities of deep learning models, and it has the unique advantage when dealing with sequential tasks. To this end, this article discusses knowledge tracing from the following four aspects. Firstly, the article briefly analyzes the characteristics and limitations of traditional knowledge tracing models. Then, through taking the development process of in-depth knowledge tracing as the main line, it summarizes knowledge tracing models based on recurrent neural networks, memory augmented neural networks, graph neural networks and their improved models; and categorizes and organizes the existing models in this field according to methods and strategies. Besides, this article sorts out the public data sets and model evaluation indicators which can be used by researchers, compares and analyzes the characteristics of different modeling methods. Finally, it discusses and prospects the future development direction of knowledge tracing based on deep learning, and lays the foundation for further in-depth knowledge tracing research.

Key words: education informatization, individualized education, knowledge tracing, deep learning, memory augmented neural network, graph neural network

摘要：

随着教育信息化程度的不断加深，以预测学生知识状态为目标的知识追踪正成为个性化教育中一项重要且富有挑战性的任务。知识追踪作为一项教育数据挖掘的时间序列任务，与深度学习模型强大的特征提取和建模能力相结合，在处理顺序任务时具有得天独厚的优势。为此，简要分析传统知识追踪模型的特点及局限性，以深度知识追踪发展历程为主线，总结基于循环神经网络、记忆增强神经网络、图神经网络的知识追踪模型及其改进模型，并对该领域的已有模型按照方法策略归类整理。同时梳理了可供研究者使用的公开数据集和模型评估指标，比较和分析不同建模方法的特点。对基于深度学习的知识追踪的未来发展方向进行探讨和展望，奠定进一步深入基于深度知识追踪研究的基础。

关键词: 教育信息化, 个性化教育, 知识追踪, 深度学习, 记忆增强神经网络, 图神经网络

LIANG Kun, REN Yimeng, SHANG Yuhu, ZHANG Yiying, WANG Cong. Review of Knowledge Tracing Preprocessing Based on Deep Learning[J]. Computer Engineering and Applications, 2021, 57(21): 41-58.

梁琨，任依梦，尚余虎，张翼英，王聪. 深度学习驱动的知识追踪研究进展综述[J]. 计算机工程与应用, 2021, 57(21): 41-58.

References

[1] ALLEN I E，SEAMAN J.Growing by degrees：online education in the United States[J].Sloan Consortium（NJ1），2005，93（12）：1984-1985.
[2] 李菲茗，叶艳伟，李晓菲，等.知识追踪模型在教育领域的应用：2008—2017年相关研究的综述[J].中国远程教育，2019（7）：86-91.
LI F M，YE Y W，LI X F，et al.Application of knowledge tracking model in education：a review from 2008 to 2017[J].Distance Education in China，2019（7）：86-91.
[3] 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.
[4] GAN W，SUN Y，YE S，et al.AI-Tutor：generating tailored remedial questions and answers based on cognitive diagnostic assessment[C]//2019 6th International Conference on Behavioral，Economic and Socio-Cultural Computing（BESC），2019.
[5] YUDELSON M V，KOEDINGER K R，GORDON G J.Individualized Bayesian knowledge tracing models[C]//16th International Conference on Artificial Intelligence in Education（AIED 2013）.Berlin，Heidelberg：Springer，2013.
[6] 张暖，江波.学习者知识追踪研究进展综述[J].计算机科学，2021，48（4）：213-222.
ZHANG N，JIANG B.Review?progress?of learner? knowledge?tracing[J].Computer Science，2021，48（4）：213-222.
[7] ROWE J P，LESTER J C.Modeling user knowledge with dynamic Bayesian networks in interactive narrative environments[C]//AAAI Conference on Artificial Intelligence & Interactive Digital Entertainment，2010.
[8] SCHODDE T，BERGMANN K，KOPP S.Adaptive robot language tutoring based on Bayesian knowledge tracing and predictive decision-making[C]//Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction，2017：128-136.
[9] KSER T，KLINGLER S，SCHWING A G，et al.Beyond knowledge tracing：modeling skill topologies with Bayesian networks[C]//International Conference on Intelligent Tutoring Systems，2014：188-198.
[10] QIU Y，QI Y，LU H，et al.Does time matter? Modeling the effect of time with Bayesian knowledge tracing[C]//International Conference on Educational Data Mining，2011.
[11] AGARWAL D，BAKER R.Dynamic knowledge tracing through data driven recency weights[C]//Proceedings of the 27th International Conference on Computers in Education，2019.
[12] CEN H.Generalized learning factors analysis：improving cognitive models with machine learning[D].Pittsburgh：Carnegie Mellon University，2009.
[13] MACLELLAN C J，RAN L，KOEDINGER K R.Accounting for slipping and other false negatives in logistic models of student learning[C]//The 8th International Conference on Educational Data Mining，2015.
[14] 叶韵.融合学习情绪的学习者知识状态建模及在中学生在线学习中的应用[D].杭州：浙江工业大学，2020.
YE Y.The research on the learner knowledge state modeling integrating academic emotion and its application in online learning in middle school[D].Hangzhou：Zhejiang University of Technology，2020.
[15] PIECH C，SPENCER J，HUANG J，et al.Deep knowledge Tracing[J].Computer Science，2015，3（3）：19-23.
[16] SCHUSTER M，PALIWAL K K.Bidirectional recurrent neural networks[J].IEEE Transactions on Signal Processing，1997，45（11）：2673-2681.
[17] HOCHREITER S，SCHMIDHUBER J.Long short-term memory[J].Neural Computation，1997，9（8）：1735-1780.
[18] CHO K，VAN MERRIENBOER B，BAHDANAU D，et al.On the properties of neural machine translation：encoder-decoder approaches[C]//Eighth Workshop on Syntax，Sementics and Structure.Doha，Qatar：ACL，2014：103-111.
[19] YUE G，BECK J E，HEFFERNAN N T.How to construct more accurate student models：comparing and optimizing knowledge tracing and performance factor analysis[J].International Journal of Artificial Intelligence in Education，2011，21（1）：27-46.
[20] GERVET T，KOEDINGER K.When is deep learning the best approach to knowledge tracing?[J].Journal of Educational Data Mining，2020，12（3）：31-54.
[21] CASALINO G，GRILLI L，LIMONE P，et al.Deep learning for knowledge tracing in learning analytics：an overview[C]//Technology Enhanced Learning Environments for Blended Education（teleXbe），2021.
[22] 刘恒宇，张天成，武培文，等.知识追踪综述[J].华东师范大学学报（自然科学版），2019（5）：1-15.
LIU H Y，ZHANG T C，WU P W，et al.A review of knowledge tracking[J].Journal of East China Normal University（Natural Science），2019（5）：1-15.
[23] PANDEY S，KARYPIS G，SRIVASTAVA J.An empirical comparison of deep learning models for knowledge tracing on large-scale dataset[J].arXiv：2101.06373，2021.
[24] SAPOUNTZI A，BHULAI S，CORNELISZ I，et al.Dynamic knowledge tracing models for large-scale adaptive learning environments[J].International Journal on Advances in Intelligent Systems，2019，12（3）：93-110.
[25] 胡学钢，刘菲，卜晨阳.教育大数据中认知跟踪模型研究进展[J].计算机研究与发展，2020，57（12）：47-70.
HU X G，LIU F，BU C Y.Research advances on knowledge tracing models in educational big data[J].Journal of Computer Research and Development，2020，57（12）：47-70.
[26] XIONG X，ZHAO S.Going deeper with deep knowledge tracing[C]//Proceedings of the 9th International Conference on Educational DataMining（EDM）.Worcester，MA：IEDMS，2016：545-550.
[27] LIANG Z，XIONG X，ZHAO S，et al.Incorporating rich features into deep knowledge tracing[C]//Proceedings of the 4th ACM Conference on Learningat Scale（L@S）.New York：ACM，2017：169-172.
[28] CHENG S，LIU Q，CHEN E.Domain adaption for knowledge tracing[J].arXiv：2001.04841，2020.
[29] RUAN S，WEI W，LANDAY J.Variational deep knowledge tracing for language learning[C]//11th International Learning Analytics and Knowledge Conference，2021.
[30] CHAN W L，YEUNG D Y.Clickstream knowledge tracing：modeling how students answer interactive online questions[C]//LAK21：11th International Learning Analytics and Knowledge Conference，2021：99-109.
[31] NAGATANI K，ZHANG Q，SATO M，et al.Augmenting knowledge tracing by considering forgetting behavior[C]//Proceedings of the International World Wide Web Conference.New York：ACM，2019：3101-3107.
[32] YANG H，CHEUNG L P.Implicit heterogeneous features embedding in deep knowledge tracing[J].Cognitive Computation，2018，10（1）：3-14.
[33] PU S，YUDELSON M，OU L，et al.Deep knowledge tracing with transformers[C]//Proceedings of the 2lst International Conference on Artificial Intelligence in Education（AIED）.Berlin：Springer，2020：252-256.
[34] CHEN P，YU L，ZHENG V W，et al.Prerequisite-driven deep knowledge tracing[C]//Proceedings of the International Conference on Data Mining（ICDM）.Piscataway，NJ：IEEE，2018：39-48.
[35] WANG Z，FENG X，TANG J，et al.Deep knowledge tracing with side information[C]//Proceedings of the International Conference on Artificial Intelligence in Education（AIED）.Berlin：Springer，2019：303-308.
[36] TONG S，LIU Q，HUANG W，et al.Structure-based knowledge tracing：an influence propagation view[C]//The 20th IEEE International Conference on Data Mining（ICDM），Sorrento，Italy，2020：17-20.
[37] BAHDANAU D，CHO K，BENGIO Y.Neural machine translation by jointly learning to align and translate[C/OL]//Proceedings of the 3rd International Conference on Learning Representations（ICLR），2015[2020-10-22].http：ll arxiv.org/abs/1409.0473.
[38] CHOROWSKI J，BA HDANAU D，SERDYUK D，et al.Attention-based models for speech recognition[J].Computer Science，2015，10（4）：429-439.
[39] XU K，BA J，KIROS R，et al.Show，attend and tell：neural image caption generation with visual attention[J].Computer Science，2015：2048-2057.
[40] SU Y，LIU Q W，LIU Q，et al.Exercise-enhanced sequential modeling for student performance prediction[C]//Proceedings of the 32nd AAAI Conference on Artificial Intelligence（AAAI）.Palo Alto，CA：AAAI，2018：2435-2443.
[41] LIU D，DAI H H，ZHANG Y P，et al.Deep knowledge tracking based on attention mechanism for student performance prediction[C]//Proceedings of the 2nd International Conference on Computer Science and Educational lnformatization（CSEI）.Piscataway，NJ：IEEE，2020：95-98.
[42] PANDEY S，KARYPIS G.A self-attentive model for knowledge tracing[C]//Proceedings of the 12th International Conference on Educational Data Mining（EDM）.Worcester，MA：IDEMS，2019[2020-10-22].https：/ldrive.google.com/file/d/18dl_X6AXkPMhiHFQ2POarstVbX\_7oMdFM.
[43] CHOI Y，LEE Y，CHO J，et al.Towards an appropriate query，key，and value computation for knowledge tracing[C]//Proceedings of the 7th ACM Conference on Learning @ Scale（L@S）.New York：ACM，2020：341-344.
[44] ARITRA G，HEFFERNAN N，LAN A S.Context-aware attentive knowledge tracing[C]//Knowledge Discovery & Data Mining，2020：2330-2339.
[45] 朱佳，张丽君，梁婉莹.数据驱动下的个性化自适应学习研究综述[J].华南师范大学学报（自然科学版），2020，52（4）：17-25.
ZHU J，ZHANG L J，LIANG W Y.A review of data-driven personalized adaptive learning[J].Journal of South China Normal University（Natural Science Edition），2020，52（4）：17-25.
[46] LEE J，YEUNG D Y.Knowledge query network for knowledge tracing：how knowledge interacts with skills[C]//Proceedings of the 9th International Conference on Learning Analytics & Knowledge（LAK）.New York：ACM，2019：491-500.
[47] CáNOVAS-SEGURA B，MORALES A，MARTíNEZ-CARRASCO A L，et al.Exploring antimicrobial resistance prediction using post-hoc interpretable methods[C]//International Workshop on Knowledge Representation for Health Care，2019.
[48] LU Y，WANG D，MENG Q，et al.Towards interpretable deep learning models for knowledge tracing[C]//Proceedings of the 21st International Conference on Artificial Intelligence in Education（AIED）.Berlin：Springer，2020：185-190.
[49] HU Q，RANGWALA H.Reliable deep grade prediction with uncertainty estimation[C]//Proceedings of the 9th International Conference on Learning Analytics & Knowledge（LAK）.New York：ACM，2019：76-85.
[50] SU Y，CHENG Z，LUO P，et al.Time-and-concept enhanced deep multidimensional item response theory for interpretable knowledge tracing[J].Knowledge-Based Systems，2021，218（1）：106819.
[51] LIU Q，HUANG Z，YIN Y，et al.EKT：exercise-aware knowledge tracing for student performance prediction[J/OL].IEEE Transactions on Knowledge and Data Engineering（TKDE），2019[2020-10-22].https：/rieeexplore.Ieee.org/abstract/document/8744302/.
[52] MINN S，YI Y，DESMARAIS M C，et al.Deep knowledge tracing and dynamic student classification for knowledge tracing[C]//Proceedings of the International Conference on Data Mining（ICDM）.Piscataway，NJ：IEEE，2018：1182-1187.
[53] VIE J J，KASHIMA H.Knowledge tracing machines：factorization machines for knowledge tracing[C]//Proceedings of the AAAI Conference on Artificial Intelligence，2019：750-757.
[54] GAN W，SUN Y，YE S，et al.Field-aware knowledge tracing machine by modelling students’ dynamic learning procedure and item difficulty[C]//2019 International Conference on Data Mining Workshops（ICDMW），2020：1045-1046.
[55] YANG S，ZHU M，HOU J，et al.Deep knowledge tracing with convolutions[J].arXiv：2008.01169，2020.
[56] TONG H，ZHOU Y，WANG Z.Exercise hierarchical feature enhanced knowledge tracing[C]//Proceedings of the 21st International Conference on Artificial Intelligence in Education（AIED）.Berlin：Springer，2020：324-328.
[57] XU L，DAVENPORT M A.Dynamic knowledge embedding and tracing[C/OL]//Proceedings of the 13th International Conference on Educational Data Mining（EDM），2020[2020-10-22].https：leducationalding.org/files/conferences/EDM2020/papers/paper_79.pdf.
[58] KHAJAH M，LINDSEY R V，MOZER M C.How deep is knowledge tracing[C]//Proceedings of the 9th International Conference on Educational Data Mining（EDM）.Worcester：MA：IEDMS，2016：94-101.
[59] XIONG X，ZHAO S，VAN INWEGEN E G，et al.Going deeper with deep knowledge tracing[C]//Proceedings of the 9th International Conference on Educational Data Mining（EDM）.Worcester，MA：IEDMS，2016：545-550.
[60] CASALINO G，GRILLI L，LIMONE P，et al.Deep learning for knowledge tracing in learning analytics：an overview[C]//Technology Enhanced Learning Environments for Blended Education（teleXbe），2021.
[61] MANDALAPU V，GONG J，CHEN L.Do we need to go deep? Knowledge tracing with big data[J].arXiv：2101.08349，2021.
[62] LIU D，ZHANG Y，ZHANG J，et al.Multiple features fusion attention mechanism enhanced deep knowledge tracing for student performance prediction[J].IEEE Access，2020，8：194894-194903.
[63] MONGKHONVANIT K，KANOPKA K，LANG D.Deep knowledge tracing and engagement with MOOCs[C]//Proceedings of the 9th International Conference on Learning Analytics & Knowledge（LAK）.New York：ACM，2019：340-342.
[64] WANG L，SY A，LIU L，et al.Deep knowledge tracing on programming exercises[C]//Proceedings of the Fourth（2017） ACM Conference on Learning@ Scale，2017：201-204.
[65] SUN Y，WANG L，XIE Q，et al.Online programming education modeling and knowledge tracing[EB/OL].[2020].https：//doi.org/10.1007/978-3-030-55130-8_23.s.
[66] ZHANG J，KING I.Topological order discovery via deep knowledge tracing[C]//International Conference on Nerual Information Processing，2016.
[67] WU Z，HE T，MAO C，et al.Exam paper generation based on performance prediction of student group[J].Information Sciences，2020，532：72-90.
[68] LALWANI A，AGRAWAL S.Validating revised bloom’s taxonomy using deep knowledge tracing[C]//Proceedings of the 19th International Conference on Artificial Intelligence in Education（AIED）.Berlin： Springer，2018：225-238.
[69] CAI D，ZHANG Y，DAI B.Learning path recommendation based on knowledge tracing model and reinforcement learning[C]//2019 IEEE 5th International Conference on Computer and Communications（ICCC）.Piscataway，NJ：IEEE，2019：1881-1885.
[70] HARIDAS M，VASUDEVAN N，GAYATHRY S，et al.Feature-aware knowledge tracing for generation of concept-knowledge reports in an intelligent tutoring system[C]//2019 IEEE Tenth International Conference on Technology for Education（T4E），2019.
[71] YEUNG C K，LIN Z，YANG K，et al.Incorporating features learned by an enhanced deep knowledge tracing model for STEM/Non-STEM job prediction[J].International Journal of Artificial Intelligence in Education，2019，29（3）：317-341.
[72] ZHANG J，SHI X，KING I，et al.Dynamic key-value memory networks for knowledge tracing[C]//Proceedings of the 26th International Conference on World Wide Web（WWW）.New York：ACM，2017：765-774.
[73] SUN X，ZHAO X，LI B，et al.Dynamic key-value memory networks with rich features for knowledge tracing[J].IEEE Transactions on Cybernetics，2021（99）：1-7.
[74] 宗晓萍，陶泽泽.基于掌握速度的知识追踪模型[J].计算机工程与应用，2021，57（6）：117-123.
ZONG X P，TAO Z Z.Knowledge tracing model based on mastery speed[J].Computer Engineering and Applications，2021，57（6）：117-123.
[75] AI F Z，CHEN Y S，GUO Y C，et al.Concept-aware deep knowledge tracing and exercise recommendation in an online learning system[C/OL]//Proceedings of the 12th International Conference on Education Data Mining（EDM），2019：240-245[2020-10-22].https：//drive.google.com/file/d/1o8JRGKx.w-d4QtM2LS2D8X2DPnq554Ix/
view?usp=drive_open.
[76] 李浩君，高鹏.融合梯度提升回归树的深度知识追踪优化模型[J].系统科学与数学，2021，20（5）：1-12.
LI H J，GAO P.Deep knowledge tracking optimization model based on gradient boosting regression tree[J].Journal of Systems Science and Mathematical Sciences，2021，20（5）：1-12.
[77] EBBINGHAUS H.Memory：a contribution to experimental psychology[J].Annals of Neurosciences，2013，20：155-156.
[78] ZOU Y，YAN X D，LI W.Knowledge tracking model based on learning process[J].Journal of Computer and Communications，2020，8（10）.
[79] ABDELRAHMAN G，WANG Q.Knowledge tracing with sequential key-value memory networks[C]//Proceedings of the 42nd International Conference on Research and Development in Information Retrieval（SIGIR）.New York：ACM，2019：175-184.
[80] CHAUDHRY R，SINGH H，DOGGA P，et al.Modeling hint-taking behavior and knowledge state of students with multi-task learning[C/OL]//Proceedings of the 11th International Conference on Educational Data Mining（EDM），2018[2020-10-22].http：//educationaldatamining.org/files/conferences/EDM2018/papers/EDM2018/_paperl_100.pdf.
[81] 李标.在线学习平台中知识追踪机制研究[D].武汉：武汉大学，2019.
LI B.Research on knowledge tracing mechanism in online learning platforms[D].Wuhan：Wuhan University，2019.
[82] MINN S，DESMARAIS M C，ZHU F，et al.Dynamic student classification on memory networks for knowledge tracing[C]//LNCS 11440：Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining（PAKDD）.Berlin：Springer，2019：163-174.
[83] SUN X，ZHAO X.Multi-behavior features based knowledge tracking using decision tree improved DKVMN[C/OL]//Proceedings of the ACM Turing Celebration Conference，2019[2020-10-22].https：//dl.acm，org/doi/abs/10.1145/3321408.3322847.
[84] TRIFA A，HEDHILI A，CHAARI W L.Knowledge tracing with an intelligent agent，in an e-learning platform[J].Education and Information Technologies，2019，24：711-741.
[85] YEUNG C K.Deep-IRT：make deep learning based knowledge tracing explainable using item response theory[C/OL]//Proceedings of the 12th International Conference on Educational Data Mining（EDM），2019[2020-10-22].https：//drive.google.com/file/d/1B_vuJGNgdl7Wxdbh9Lz9nkM_
GuxWAfGD.
[86] WOODWORTH R S，THORNDIKE E L.The influence of improvement in one mental function upon the efficiency of other functions.II.The estimation of magnitudes[J].Psychological Review，1913，8（3）：247-261.
[87] NAKAGAWA H，IWASAWA Y，MATSUO Y.Graph-based knowledge tracing：modeling student proficiency using graph neural network[C]//Proceedings of the International Conference on Web Intelligence（WI）.Piscataway，NJ：IEEE，2019：156-163.
[88] TONG H，ZHOU Y，WANG Z.HGKT：introducing problem schema with hierarchical exercise graph for knowledge tracing[J].arXiv：2006.16915，2020.
[89] DEFFERRARD M，BRESSON X，VANDERGHEYNST P.Convolutional neural networks on graphs with fast localized spectral filtering[C]//Advances in Neural Information Processing Systems 29，Barcelona，Spain，December 5-10，2016：240-245.
[90] KIP F，WELLING M.Semi-supervised classification with graph convolutional networks[J].arXiv：1609.02907，2016.
[91] YANG Y，SHEN J，QU Y，et al.GIKT：a graph-based interaction model for knowledge tracing[J].arXiv：2009. 05991，2020.
[92] 王健宗，孔令炜，黄章成，等.图神经网络综述[J].计算机工程，2021，47（4）：1-12.
WANG J Z，KONG L W，HUANG Z C，et al.Survey of graph neural network[J].Computer Engineering，2021，47（4）：1-12.
[93] CáNOVAS-SEGURA B，MORALES A，MARTíNEZ-CARRASCO A L，et al.Exploring antimicrobial resistance prediction using post-hoc interpretable methods[M]//Artificial intelligence in medicine：knowledge representation and transparent and explainable systems.Cham：Springer，2019：93-107.