{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:41:32Z","timestamp":1783737692279,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":52,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,7,6]],"date-time":"2022-07-06T00:00:00Z","timestamp":1657065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["No.2021YFF0901003"],"award-info":[{"award-number":["No.2021YFF0901003"]}]},{"name":"the National Natural Science Foundation of China","award":["No.U20A20229, No.61922073, and No.62106244"],"award-info":[{"award-number":["No.U20A20229, No.61922073, and No.62106244"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3477495.3531939","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:08Z","timestamp":1657206728000},"page":"427-437","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":100,"title":["Assessing Student's Dynamic Knowledge State by Exploring the Question Difficulty Effect"],"prefix":"10.1145","author":[{"given":"Shuanghong","family":"Shen","sequence":"first","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Su","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Hefei Normal University &amp; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shijin","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Cognitive Intelligence &amp; iFLYTEK AI Research (Central China), iFLYTEK Co., Ltd, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China &amp; State Key Laboratory of Cognitive Intelligence, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Curtis","author":"Alagumalai Sivakumar","year":"2005","unstructured":"Sivakumar Alagumalai and David D . Curtis . 2005 . Classical Test Theory. Springer Netherlands , Dordrecht, 1--14. Sivakumar Alagumalai and David D. Curtis. 2005. Classical Test Theory. Springer Netherlands, Dordrecht, 1--14."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2566486.2568042"},{"key":"e_1_3_2_2_3_1","volume-title":"User Modeling","author":"Beck Joseph","unstructured":"Joseph Beck , Mia Stern , and Beverly Park Woolf . 1997. Using the student model to control problem difficulty . In User Modeling . Springer , 277--288. Joseph Beck, Mia Stern, and Beverly Park Woolf. 1997. Using the student model to control problem difficulty. In User Modeling. Springer, 277--288."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/11774303_17"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01099821"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1111\/bjet.12109_2"},{"key":"e_1_3_2_2_7_1","volume-title":"Filip Van Opstal, and Eva Van den Bussche","author":"Desender Kobe","year":"2017","unstructured":"Kobe Desender , Filip Van Opstal, and Eva Van den Bussche . 2017 . Subjective experience of difficulty depends on multiple cues. Scientific reports, Vol. 7 , 1 (2017), 1--14. Kobe Desender, Filip Van Opstal, and Eva Van den Bussche. 2017. Subjective experience of difficulty depends on multiple cues. Scientific reports, Vol. 7, 1 (2017), 1--14."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-011-9106-8"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11257-009-9063-7"},{"key":"e_1_3_2_2_10_1","volume-title":"Lan","author":"Ghosh Aritra","year":"2020","unstructured":"Aritra Ghosh , Neil Heffernan , and Andrew S . Lan . 2020 . Context-Aware Attentive Knowledge Tracing (KDD '20). New York, NY, USA , 2330--2339. Aritra Ghosh, Neil Heffernan, and Andrew S. Lan. 2020. Context-Aware Attentive Knowledge Tracing (KDD '20). New York, NY, USA, 2330--2339."},{"key":"e_1_3_2_2_11_1","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256.  Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In AISTATS. 249--256."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W19-4402"},{"key":"e_1_3_2_2_13_1","volume-title":"Long short-term memory. Neural computation","author":"Hochreiter Sepp","year":"1997","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber . 1997. Long short-term memory. Neural computation , Vol. 9 , 8 ( 1997 ), 1735--1780. Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, Vol. 9, 8 (1997), 1735--1780."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"crossref","unstructured":"Zhenya Huang Qi Liu Enhong Chen Hongke Zhao Mingyong Gao Si Wei Yu Su and Guoping Hu. 2017. Question Difficulty Prediction for READING Problems in Standard Tests. In AAAI .  Zhenya Huang Qi Liu Enhong Chen Hongke Zhao Mingyong Gao Si Wei Yu Su and Guoping Hu. 2017. Question Difficulty Prediction for READING Problems in Standard Tests. In AAAI .","DOI":"10.1609\/aaai.v31i1.10740"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0360-1315(02)00121-5"},{"key":"e_1_3_2_2_16_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_17_1","first-page":"181","article-title":"Question difficulty and respondents' cognitive ability: The effect on data quality","volume":"13","author":"Kn\u00e4uper Barbel","year":"1997","unstructured":"Barbel Kn\u00e4uper , Robert F Belli , Daniel H Hill , and A Regula Herzog . 1997 . Question difficulty and respondents' cognitive ability: The effect on data quality . JOURNAL OF OFFICIAL STATISTICS-STOCKHOLM- , Vol. 13 (1997), 181 -- 199 . Barbel Kn\u00e4uper, Robert F Belli, Daniel H Hill, and A Regula Herzog. 1997. Question difficulty and respondents' cognitive ability: The effect on data quality. JOURNAL OF OFFICIAL STATISTICS-STOCKHOLM-, Vol. 13 (1997), 181--199.","journal-title":"JOURNAL OF OFFICIAL STATISTICS-STOCKHOLM-"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.trit.2016.12.001"},{"key":"e_1_3_2_2_19_1","volume-title":"Deep learning. nature","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015. Deep learning. nature , Vol. 521 , 7553 ( 2015 ), 436. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. nature, Vol. 521, 7553 (2015), 436."},{"key":"e_1_3_2_2_20_1","volume-title":"A Survey of Knowledge Tracing. arXiv preprint arXiv:2105.15106","author":"Liu Qi","year":"2021","unstructured":"Qi Liu , Shuanghong Shen , Zhenya Huang , Enhong Chen , and Yonghe Zheng . 2021. A Survey of Knowledge Tracing. arXiv preprint arXiv:2105.15106 ( 2021 ). Qi Liu, Shuanghong Shen, Zhenya Huang, Enhong Chen, and Yonghe Zheng. 2021. A Survey of Knowledge Tracing. arXiv preprint arXiv:2105.15106 (2021)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330922"},{"key":"e_1_3_2_2_22_1","volume-title":"International Joint Conferences on Artificial Intelligence Organization, 1577--1583","author":"Liu Yunfei","year":"2020","unstructured":"Yunfei Liu , Yang Yang , Xianyu Chen , Jian Shen , Haifeng Zhang , and Yong Yu . 2020 . Improving Knowledge Tracing via Pre-training Question Embeddings, Christian Bessiere (Ed.) . International Joint Conferences on Artificial Intelligence Organization, 1577--1583 . Yunfei Liu, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, and Yong Yu. 2020. Improving Knowledge Tracing via Pre-training Question Embeddings, Christian Bessiere (Ed.). International Joint Conferences on Artificial Intelligence Organization, 1577--1583."},{"key":"e_1_3_2_2_23_1","volume-title":"Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM.","author":"Lomas Derek","unstructured":"Derek Lomas , Kishan Patel , Jodi L. Forlizzi , and Kenneth R. Koedinger . 2013. Optimizing challenge in an educational game using large-scale design experiments . In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM. Derek Lomas, Kishan Patel, Jodi L. Forlizzi, and Kenneth R. Koedinger. 2013. Optimizing challenge in an educational game using large-scale design experiments. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3404835.3462827"},{"key":"e_1_3_2_2_25_1","unstructured":"Frederique M Lord. 1980. Applications of Item Response Theory to Practical Testing Problems. LAWRENCE ERLBAUM ASSCCIAATES.  Frederique M Lord. 1980. Applications of Item Response Theory to Practical Testing Problems. LAWRENCE ERLBAUM ASSCCIAATES."},{"key":"e_1_3_2_2_26_1","volume-title":"Applications of item response theory to practical testing problems","author":"Lord Frederic M","unstructured":"Frederic M Lord . 2012. Applications of item response theory to practical testing problems . Routledge . Frederic M Lord. 2012. Applications of item response theory to practical testing problems .Routledge."},{"key":"e_1_3_2_2_27_1","volume-title":"In: Proceedings of the 12th International Conference on Educational Data Mining (EDM","author":"Mao Ye","year":"2019","unstructured":"Ye Mao . 2019 . One minute is enough: Early prediction of student success and event-level difficulty during novice programming tasks . In In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019). Ye Mao. 2019. One minute is enough: Early prediction of student success and event-level difficulty during novice programming tasks. In In: Proceedings of the 12th International Conference on Educational Data Mining (EDM 2019)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDMW.2018.00220"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics.","author":"Pado Ulrike","year":"2017","unstructured":"Ulrike Pado . 2017 . Question Difficulty textendash How to Estimate Without Norming, How to Use for Automated Grading . In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics. Ulrike Pado. 2017. Question Difficulty textendash How to Estimate Without Norming, How to Use for Automated Grading. In Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications. Association for Computational Linguistics."},{"key":"e_1_3_2_2_30_1","volume-title":"A Self-Attentive model for Knowledge Tracing. arXiv preprint arXiv:1907.06837","author":"Pandey Shalini","year":"2019","unstructured":"Shalini Pandey and George Karypis . 2019. A Self-Attentive model for Knowledge Tracing. arXiv preprint arXiv:1907.06837 ( 2019 ). Shalini Pandey and George Karypis. 2019. A Self-Attentive model for Knowledge Tracing. arXiv preprint arXiv:1907.06837 (2019)."},{"key":"e_1_3_2_2_31_1","volume-title":"RKT: Relation-Aware Self-Attention for Knowledge Tracing (CIKM '20)","author":"Pandey Shalini","year":"2020","unstructured":"Shalini Pandey and Jaideep Srivastava . 2020 . RKT: Relation-Aware Self-Attention for Knowledge Tracing (CIKM '20) . New York, NY, USA, 1205--1214. Shalini Pandey and Jaideep Srivastava. 2020. RKT: Relation-Aware Self-Attention for Knowledge Tracing (CIKM '20). New York, NY, USA, 1205--1214."},{"key":"e_1_3_2_2_32_1","volume-title":"International Conference on Intelligent Tutoring Systems. Springer, 267--272","author":"Jan Papouvs","year":"2016","unstructured":"Jan Papouvs ek, V\u00e8t Stanislav , and Radek Pel\u00e1nek . 2016 . Impact of question difficulty on engagement and learning . In International Conference on Intelligent Tutoring Systems. Springer, 267--272 . Jan Papouvs ek, V\u00e8t Stanislav, and Radek Pel\u00e1nek. 2016. Impact of question difficulty on engagement and learning. In International Conference on Intelligent Tutoring Systems. Springer, 267--272."},{"key":"e_1_3_2_2_33_1","first-page":"137","article-title":"Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX","volume":"13","author":"Pardos Zachary A","year":"2013","unstructured":"Zachary A Pardos , Yoav Bergner , Daniel T Seaton , and David E Pritchard . 2013 . Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX . EDM , Vol. 13 (2013), 137 -- 144 . Zachary A Pardos, Yoav Bergner, Daniel T Seaton, and David E Pritchard. 2013. Adapting Bayesian Knowledge Tracing to a Massive Open Online Course in edX. EDM, Vol. 13 (2013), 137--144.","journal-title":"EDM"},{"key":"e_1_3_2_2_34_1","volume-title":"Heffernan","author":"Pardos Zachary A.","year":"2011","unstructured":"Zachary A. Pardos and Neil T . Heffernan . 2011 . KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model. In User Modeling, Adaption and Personalization. Vol. 6787 . Berlin, Heidelberg , 243--254. Zachary A. Pardos and Neil T. Heffernan. 2011. KT-IDEM: Introducing Item Difficulty to the Knowledge Tracing Model. In User Modeling, Adaption and Personalization. Vol. 6787. Berlin, Heidelberg, 243--254."},{"key":"e_1_3_2_2_35_1","volume-title":"Performance Factors Analysis--A New Alternative to Knowledge Tracing. Online Submission","author":"Pavlik Philip I","year":"2009","unstructured":"Philip I Pavlik Jr , Hao Cen , and Kenneth R Koedinger . 2009. Performance Factors Analysis--A New Alternative to Knowledge Tracing. Online Submission ( 2009 ). Philip I Pavlik Jr, Hao Cen, and Kenneth R Koedinger. 2009. Performance Factors Analysis--A New Alternative to Knowledge Tracing. Online Submission (2009)."},{"key":"e_1_3_2_2_36_1","unstructured":"Chris Piech Jonathan Bassen Jonathan Huang Surya Ganguli Mehran Sahami Leonidas J Guibas and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In NeurIPS. 505--513.  Chris Piech Jonathan Bassen Jonathan Huang Surya Ganguli Mehran Sahami Leonidas J Guibas and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In NeurIPS. 505--513."},{"key":"e_1_3_2_2_37_1","volume-title":"EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction","author":"Qi Liu","year":"2021","unstructured":"Liu Qi , Huang Zhenya , Yin Yu , Chen Enhong , Xiong Hui , Su Yu , and Hu Guoping . 2021 . EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction . IEEE Transactions on Knowledge and Data Engineering ( 2021). Liu Qi, Huang Zhenya, Yin Yu, Chen Enhong, Xiong Hui, Su Yu, and Hu Guoping. 2021. EKT: Exercise-Aware Knowledge Tracing for Student Performance Prediction. IEEE Transactions on Knowledge and Data Engineering (2021)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467237"},{"key":"e_1_3_2_2_39_1","unstructured":"Shuanghong Shen Qi Liu Enhong Chen Han Wu Zhenya Huang Weihao Zhao Yu Su Haiping Ma and Shijin Wang. 2020. Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process. SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July 2020 (2020) 1857--1860.  Shuanghong Shen Qi Liu Enhong Chen Han Wu Zhenya Huang Weihao Zhao Yu Su Haiping Ma and Shijin Wang. 2020. Convolutional Knowledge Tracing: Modeling Individualization in Student Learning Process. SIGIR '20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval Virtual Event China July 2020 (2020) 1857--1860."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"crossref","unstructured":"Nguyen Thai-Nghe Lucas Drumond Tom\u00e1vs Horv\u00e1th Artus Krohn-Grimberghe Alexandros Nanopoulos and Lars Schmidt-Thieme. 2012. Factorization techniques for predicting student performance. In Educational recommender systems and technologies: Practices and challenges. IGI Global 129--153.  Nguyen Thai-Nghe Lucas Drumond Tom\u00e1vs Horv\u00e1th Artus Krohn-Grimberghe Alexandros Nanopoulos and Lars Schmidt-Thieme. 2012. Factorization techniques for predicting student performance. In Educational recommender systems and technologies: Practices and challenges. IGI Global 129--153.","DOI":"10.4018\/978-1-61350-489-5.ch006"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00063"},{"key":"e_1_3_2_2_42_1","volume-title":"Handbook of modern item response theory","author":"Van Der Linden Wim J","unstructured":"Wim J Van Der Linden and Ronald K Hambleton . 1997. Item response theory: Brief history, common models, and extensions . In Handbook of modern item response theory . Springer , 1--28. Wim J Van Der Linden and Ronald K Hambleton. 1997. Item response theory: Brief history, common models, and extensions. In Handbook of modern item response theory. Springer, 1--28."},{"key":"e_1_3_2_2_43_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008.  Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems. 5998--6008."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301750"},{"key":"e_1_3_2_2_45_1","volume-title":"Temporal Cross-Effects in Knowledge Tracing (The International Conference on Web Search and Data Mining). Association for Computing Machinery","author":"Wang Chenyang","year":"2021","unstructured":"Chenyang Wang , Weizhi Ma , Min Zhang , Chuancheng Lv , Fengyuan Wan , Huijie Lin , Taoran Tang , Yiqun Liu , and Shaoping Ma . 2021 . Temporal Cross-Effects in Knowledge Tracing (The International Conference on Web Search and Data Mining). Association for Computing Machinery , New York, NY, USA, 517--525. Chenyang Wang, Weizhi Ma, Min Zhang, Chuancheng Lv, Fengyuan Wan, Huijie Lin, Taoran Tang, Yiqun Liu, and Shaoping Ma. 2021. Temporal Cross-Effects in Knowledge Tracing (The International Conference on Web Search and Data Mining). Association for Computing Machinery, New York, NY, USA, 517--525."},{"key":"e_1_3_2_2_46_1","volume-title":"Neural Cognitive Diagnosis for Intelligent Education Systems. In AAAI 2020 .","author":"Wang Fei","year":"2020","unstructured":"Fei Wang , Qi Liu , Enhong Chen , Zhenya Huang , Yuying Chen , Yu Yin , Zai Huang , and Shijin Wang . 2020 b . Neural Cognitive Diagnosis for Intelligent Education Systems. In AAAI 2020 . Fei Wang, Qi Liu, Enhong Chen, Zhenya Huang, Yuying Chen, Yu Yin, Zai Huang, and Shijin Wang. 2020 b. Neural Cognitive Diagnosis for Intelligent Education Systems. In AAAI 2020 ."},{"key":"e_1_3_2_2_47_1","unstructured":"Zichao Wang Angus Lamb Evgeny Saveliev Pashmina Cameron Yordan Zaykov Jos\u00e9 Miguel Hern\u00e1ndez-Lobato Richard E Turner Richard G Baraniuk Craig Barton Simon Peyton Jones Simon Woodhead and Cheng Zhang. 2020 a. Diagnostic questions: The neurips 2020 education challenge. arXiv preprint arXiv:2007.12061 (2020).  Zichao Wang Angus Lamb Evgeny Saveliev Pashmina Cameron Yordan Zaykov Jos\u00e9 Miguel Hern\u00e1ndez-Lobato Richard E Turner Richard G Baraniuk Craig Barton Simon Peyton Jones Simon Woodhead and Cheng Zhang. 2020 a. Diagnostic questions: The neurips 2020 education challenge. arXiv preprint arXiv:2007.12061 (2020)."},{"key":"e_1_3_2_2_48_1","volume-title":"A learning algorithm for continually running fully recurrent neural networks. Neural computation","author":"Williams Ronald J","year":"1989","unstructured":"Ronald J Williams and David Zipser . 1989. A learning algorithm for continually running fully recurrent neural networks. Neural computation , Vol. 1 , 2 ( 1989 ), 270--280. Ronald J Williams and David Zipser. 1989. A learning algorithm for continually running fully recurrent neural networks. Neural computation, Vol. 1, 2 (1989), 270--280."},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1145\/3231644.3231647"},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"crossref","unstructured":"Jiani Zhang Xingjian Shi Irwin King and Dit-Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. In WWW. 765--774.  Jiani Zhang Xingjian Shi Irwin King and Dit-Yan Yeung. 2017. Dynamic key-value memory networks for knowledge tracing. In WWW. 765--774.","DOI":"10.1145\/3038912.3052580"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482372"}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Madrid Spain","acronym":"SIGIR '22","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531939","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3531939","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:10:19Z","timestamp":1750183819000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531939"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":52,"alternative-id":["10.1145\/3477495.3531939","10.1145\/3477495"],"URL":"https:\/\/doi.org\/10.1145\/3477495.3531939","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}