{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T19:50:02Z","timestamp":1783799402874,"version":"3.55.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T00:00:00Z","timestamp":1664928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62077009"],"award-info":[{"award-number":["62077009"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"State Key Laboratory of Cognitive Intelligence","award":["iED2019-Z04"],"award-info":[{"award-number":["iED2019-Z04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,1]]},"DOI":"10.1007\/s00521-022-07834-w","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T18:19:27Z","timestamp":1664993967000},"page":"1819-1833","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Knowledge tracing based on multi-feature fusion"],"prefix":"10.1007","volume":"35","author":[{"given":"Yongkang","family":"Xiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0945-854X","authenticated-orcid":false,"given":"Rong","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yixin","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"7834_CR1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1007\/BF01099821","volume":"4","author":"AT Corbett","year":"1994","unstructured":"Corbett AT, Anderson JR (1994) Knowledge tracing: modeling the acquisition of procedural knowledge. User Model User Adapt Interact 4:253\u2013278","journal-title":"User Model User Adapt Interact"},{"issue":"439","key":"7834_CR2","doi-asserted-by":"publisher","first-page":"1227","DOI":"10.2307\/2965612","volume":"92","author":"M Wells","year":"1997","unstructured":"Wells M, Van der Linden WJ, Hambleton RK (1997) Handbook of modern item response theory. J Am Stat Assoc 92(439):1227. https:\/\/doi.org\/10.2307\/2965612","journal-title":"J Am Stat Assoc"},{"key":"7834_CR3","doi-asserted-by":"publisher","unstructured":"Yudelson MV, Koedinger KR, Gordon GJ (2013) Individualized Bayesian knowledge tracing models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Lecture Notes in Bioinformatics), vol 7926 LNAI, pp 171\u2013180, 2013. https:\/\/doi.org\/10.1007\/978-3-642-39112-5_18","DOI":"10.1007\/978-3-642-39112-5_18"},{"key":"7834_CR4","first-page":"505","volume":"1","author":"C Piech","year":"2015","unstructured":"Piech C et al (2015) Deep knowledge tracing. Adv Neural Inf Process Syst 1:505\u2013513","journal-title":"Adv Neural Inf Process Syst"},{"key":"7834_CR5","doi-asserted-by":"publisher","unstructured":"Zhang J, Shi X, King I, Yeung DY (2017) Dynamic key-value memory networks for knowledge tracing. In: 26th international world wide web conference WWW 2017, pp 765\u2013774, 2017. https:\/\/doi.org\/10.1145\/3038912.3052580","DOI":"10.1145\/3038912.3052580"},{"key":"7834_CR6","unstructured":"Birnbaum AL (1968) Some latent trait models and their use in inferring an examinee\u2019s ability. In: Statistical theories of mental test scores, pp 395\u2013479, 1968"},{"key":"7834_CR7","unstructured":"Wilson KH, Karklin Y, Han B, Ekanadham C (2016) Back to the basics: Bayesian extensions of IRT outperform neural networks for proficiency estimation. In: Proceedings of the 9th international conference on educational data mining, EDM 2016, pp 539\u2013544, 2016"},{"issue":"3","key":"7834_CR8","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1023\/B:QURE.0000021503.45367.f2","volume":"13","author":"P Fayers","year":"2004","unstructured":"Fayers P (2004) Item response theory for psychologists. Qual Life Res 13(3):715\u2013716. https:\/\/doi.org\/10.1023\/B:QURE.0000021503.45367.f2","journal-title":"Qual Life Res"},{"key":"7834_CR9","unstructured":"Yao L, Schwarz RD (2005) A multidimensional partial credit model with associated item and test statistics. Paper presented at 2005 the Annual Meeting of the American Educational Research Association, Montreal, Canada, pp 1\u201342, 2005"},{"key":"7834_CR10","doi-asserted-by":"publisher","unstructured":"Baker RSJD, Corbett AT, Aleven V (2008) More accurate student modeling through contextual estimation of slip and guess probabilities in Bayesian knowledge tracing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Lecture Notes in Bioinformatics), vol 5091 LNCS, pp 406\u2013415, 2008. https:\/\/doi.org\/10.1007\/978-3-540-69132-7_44","DOI":"10.1007\/978-3-540-69132-7_44"},{"issue":"7553","key":"7834_CR11","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"7834_CR12","doi-asserted-by":"publisher","first-page":"85","DOI":"10.1016\/j.neunet.2014.09.003","volume":"61","author":"J Schmidhuber","year":"2015","unstructured":"Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85\u2013117. https:\/\/doi.org\/10.1016\/j.neunet.2014.09.003","journal-title":"Neural Netw"},{"issue":"8","key":"7834_CR13","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735","journal-title":"Neural Comput"},{"key":"7834_CR14","unstructured":"Khajah MM, Wing RM, Lindsey RV, Mozer MC (2014) Integrating latent-factor and knowledge-tracing models to predict individual differences in learning. In: Proceedings of the 7th international conference on educational data mining, no. Edm, pp 99\u2013106, 2014"},{"key":"7834_CR15","unstructured":"Khajah MM, Huang Y, Gonz\u00e1lez-Brenes JP, Mozer MC, Brusilovsky P (2014) Integrating knowledge tracing and item response theory: a tale of two frameworks. In: CEUR workshop proceeding, vol 1181, pp 7\u201315, 2014"},{"key":"7834_CR16","doi-asserted-by":"publisher","unstructured":"Pardos ZA, Heffernan NT (2011) KT-IDEM: introducing item difficulty to the knowledge tracing model. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Lecture Notes in Bioinformatics), vol 6787 LNCS, pp 243\u2013254, 2011. https:\/\/doi.org\/10.1007\/978-3-642-22362-4_21","DOI":"10.1007\/978-3-642-22362-4_21"},{"key":"7834_CR17","doi-asserted-by":"publisher","unstructured":"Yeung CK, Yeung DY (2018) Addressing two problems in deep knowledge tracing via prediction-consistent regularization. In: Proceedings of the 5th annual ACM conference on learning at scale, L S 2018, 2018. https:\/\/doi.org\/10.1145\/3231644.3231647","DOI":"10.1145\/3231644.3231647"},{"key":"7834_CR18","doi-asserted-by":"publisher","unstructured":"Minn S, Yu Y, Desmarais MC, Zhu F, Vie JJ (2018) Deep knowledge tracing and dynamic student classification for knowledge tracing. In: Proceedings of IEEE international conference on data mining, ICDM, vol 2018-Nov, pp 1182\u20131187, 2018. https:\/\/doi.org\/10.1109\/ICDM.2018.00156","DOI":"10.1109\/ICDM.2018.00156"},{"key":"7834_CR19","doi-asserted-by":"publisher","unstructured":"Zhang L, Xiong X, Zhao S, Botelho A, Heffernan NT (2017) Incorporating rich features into deep knowledge tracing. In: L@S 2017\u2014proceedings of the 4th ACM conference on learning scale, pp 169\u2013172, 2017. https:\/\/doi.org\/10.1145\/3051457.3053976","DOI":"10.1145\/3051457.3053976"},{"issue":"1","key":"7834_CR20","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s12559-017-9522-0","volume":"10","author":"H Yang","year":"2018","unstructured":"Yang H, Cheung LP (2018) Implicit heterogeneous features embedding in deep knowledge tracing. Cognit Comput 10(1):3\u201314. https:\/\/doi.org\/10.1007\/s12559-017-9522-0","journal-title":"Cognit Comput"},{"key":"7834_CR21","doi-asserted-by":"publisher","unstructured":"Liu Y, Yang Y, Chen X, Shen J, Zhang H, Yu Y (2020) Improving knowledge tracing via pre-training question embeddings. In: IJCAI IJCAI international joint conferences on artificial intelligence, pp 1577\u20131583. https:\/\/doi.org\/10.24963\/ijcai.2020\/219","DOI":"10.24963\/ijcai.2020\/219"},{"issue":"1","key":"7834_CR22","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1109\/TKDE.2019.2924374","volume":"33","author":"Q Liu","year":"2021","unstructured":"Liu Q et al (2021) EKT: exercise-aware knowledge tracing for student performance prediction. IEEE Trans Knowl Data Eng 33(1):100\u2013115. https:\/\/doi.org\/10.1109\/TKDE.2019.2924374","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7834_CR23","doi-asserted-by":"publisher","unstructured":"Tong H, Zhou Y, Wang Z (2020) Exercise hierarchical feature enhanced knowledge tracing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence Lecture Notes in Bioinformatics), vol 12164 LNAI, pp 324\u2013328, 2020. https:\/\/doi.org\/10.1007\/978-3-030-52240-7_59","DOI":"10.1007\/978-3-030-52240-7_59"},{"key":"7834_CR24","unstructured":"Khajah M, Lindsey RV, Mozer MC (2016) How deep is knowledge tracing? In: Proceedings of 9th international conference on educational data mining, EDM 2016, pp 94\u2013101, 2016"},{"key":"7834_CR25","unstructured":"Xiong X, Zhao S, Van Inwegen EG, Beck JE (2016) Going deeper with deep knowledge tracing. In: Proceedings of 9th international conference on educational data mining, EDM 2016, pp 545\u2013550, 2016"},{"key":"7834_CR26","unstructured":"Sonkar S, Waters AE, Lan AS, Grimaldi PJ, Baraniuk RG (2020) qDKT: question-centric deep knowledge tracing, 2020 [Online]. http:\/\/arxiv.org\/abs\/2005.12442"},{"key":"7834_CR27","doi-asserted-by":"publisher","unstructured":"Song W et al (2019) Autoint: automatic feature interaction learning via self-attentive neural networks. In: International conference on information and knowledge management, proceedings, pp 1161\u20131170, 2019. https:\/\/doi.org\/10.1145\/3357384.3357925","DOI":"10.1145\/3357384.3357925"},{"key":"7834_CR28","unstructured":"Ling CX, Huang J, Zhang H (2003) AUC: a statistically consistent and more discriminating measure than accuracy. In: IJCAI international joint conferences on artificial intelligence, pp 519\u2013524, 2003"},{"key":"7834_CR29","doi-asserted-by":"crossref","unstructured":"Minn S, Desmarais MC, Zhu F, Xiao J, Wang J (2019) Dynamic student classification on memory networks for knowledge tracing. In: Pacific-Asia conference on knowledge discovery and data mining, vol 11440 LNAI, 2019, pp 163\u2013174","DOI":"10.1007\/978-3-030-16145-3_13"},{"key":"7834_CR30","unstructured":"Yeung CK (2019) Deep-IRT: make deep learning based knowledge tracing explainable using item response theory. In: EDM 2019\u2014proceedings of 12th international conference on educational data mining, pp 683\u2013686, 2019"},{"key":"7834_CR31","unstructured":"Ha H, Hwang U, Hong Y, Jang J, Yoon S (2018) Deep trustworthy knowledge tracing, 2018 [Online]. http:\/\/arxiv.org\/abs\/1805.10768."},{"key":"7834_CR32","doi-asserted-by":"publisher","unstructured":"Abdelrahman G, Wang Q (2019) Knowledge tracing with sequential key-value memory networks. In: SIGIR 2019\u2014proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval, pp 175\u2013184, 2019. https:\/\/doi.org\/10.1145\/3331184.3331195","DOI":"10.1145\/3331184.3331195"},{"key":"7834_CR33","doi-asserted-by":"publisher","unstructured":"Kim Y (2014) Convolutional neural networks for sentence classification. In: EMNLP 2014\u20142014 conference on empirical methods in natural language processing conference, pp 1746\u20131751, 2014. https:\/\/doi.org\/10.3115\/v1\/d14-1181","DOI":"10.3115\/v1\/d14-1181"},{"key":"7834_CR34","unstructured":"Seo PH, Lin Z, Cohen S, Shen X, Han B (2016) Hierarchical attention networks. ArXiv, pp 1480\u20131489, 2016, [Online]. http:\/\/arxiv.org\/abs\/1606.02393."},{"key":"7834_CR35","unstructured":"Vaswani A et al. (2017) Attention is all you need. Adv Neural Inf Process Syst 5999\u20136009"},{"key":"7834_CR36","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K (2019) BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, vol 1 (long and short papers), Minneapolis, Minnesota, June 2019. Association for Computational Linguistics, pp 4171\u20134186. https:\/\/doi.org\/10.18653\/v1\/N19-142","DOI":"10.18653\/v1\/N19-142"},{"key":"7834_CR37","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient estimation of word representations in vector space. In: 1st international conference on learning representations. ICLR 2013\u2014workshop track proceedings, pp 1\u201312, 2013"},{"key":"7834_CR38","doi-asserted-by":"publisher","unstructured":"Koedinger KR, Baker RSJD, Cunningham K, Skogsholm A, Leber B, Stamper J (2010) A data repository for the EDM community: the PSLC datashop. In: Handbook of educational data mining, pp 43\u201356, 2010. https:\/\/doi.org\/10.1201\/b10274","DOI":"10.1201\/b10274"},{"key":"7834_CR39","unstructured":"Kingma DP, Ba JL (2015) Adam: a method for stochastic optimization. In: 3rd international conference on learning representations. ICLR 2015\u2014conference track proceedings, pp 1\u201313, 2015"},{"key":"7834_CR40","unstructured":"Abadi M et al (2016) TensorFlow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX symposium on operating systems design and implementation, OSDI 2016, pp 265\u2013283, 2016"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07834-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07834-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07834-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T17:28:51Z","timestamp":1673285331000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07834-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,5]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2023,1]]}},"alternative-id":["7834"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07834-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,5]]},"assertion":[{"value":"25 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}