{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T13:35:35Z","timestamp":1778852135593,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":49,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CCF-Tencent Open Research Fund","award":["RAGR20200121"],"award-info":[{"award-number":["RAGR20200121"]}]},{"name":"National Key Research and Development Program of China","award":["2018YFC0832101"],"award-info":[{"award-number":["2018YFC0832101"]}]},{"name":"Foundation of State Key Laboratory of Cognitive Intelligence","award":["iED2020-M004"],"award-info":[{"award-number":["iED2020-M004"]}]},{"name":"National Natural Science Foundation of China","award":["61922073, 61976198, 62022077"],"award-info":[{"award-number":["61922073, 61976198, 62022077"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,8]]},"DOI":"10.1145\/3437963.3441747","type":"proceedings-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:36:17Z","timestamp":1615005377000},"page":"662-670","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["Federated Deep Knowledge Tracing"],"prefix":"10.1145","author":[{"given":"Jinze","family":"Wu","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Defu","family":"Lian","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Haiping","family":"Ma","sequence":"additional","affiliation":[{"name":"Anhui University, Hefei, China"}]},{"given":"Shijin","family":"Wang","sequence":"additional","affiliation":[{"name":"iFLYTEK Co., Ltd., Hefei, China"}]}],"member":"320","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3331184.3331195"},{"key":"e_1_3_2_1_2_1","first-page":"64","article-title":"The relationship between CTT and IRT approaches in Analyzing Item Characteristics","volume":"1","author":"Abedalaziz Nabeel","year":"2018","unstructured":"Nabeel Abedalaziz and Chin Hai Leng . 2018 . The relationship between CTT and IRT approaches in Analyzing Item Characteristics . MOJES: Malaysian Online Journal of Educational Sciences , Vol. 1 , 1 (2018), 64 -- 70 . Nabeel Abedalaziz and Chin Hai Leng. 2018. The relationship between CTT and IRT approaches in Analyzing Item Characteristics. MOJES: Malaysian Online Journal of Educational Sciences, Vol. 1, 1 (2018), 64--70.","journal-title":"MOJES: Malaysian Online Journal of Educational Sciences"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/0022-2496(69)90005-4"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-2604-3_16"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132929"},{"key":"e_1_3_2_1_6_1","volume-title":"Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction","author":"Corbett Albert T","year":"1994","unstructured":"Albert T Corbett and John R Anderson . 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction , Vol. 4 , 4 ( 1994 ), 253--278. Albert T Corbett and John R Anderson. 1994. Knowledge tracing: Modeling the acquisition of procedural knowledge. User modeling and user-adapted interaction, Vol. 4, 4 (1994), 253--278."},{"key":"e_1_3_2_1_7_1","volume-title":"Coefficient alpha and the internal structure of tests. psychometrika","author":"Cronbach Lee J","year":"1951","unstructured":"Lee J Cronbach . 1951. Coefficient alpha and the internal structure of tests. psychometrika , Vol. 16 , 3 ( 1951 ), 297--334. Lee J Cronbach. 1951. Coefficient alpha and the internal structure of tests. psychometrika, Vol. 16, 3 (1951), 297--334."},{"key":"e_1_3_2_1_8_1","unstructured":"Barbara B Ellis and Alan D Mead. 2002. Item analysis: Theory and practice using classical and modern test theory. (2002).  Barbara B Ellis and Alan D Mead. 2002. Item analysis: Theory and practice using classical and modern test theory. (2002)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Harold Gulliksen. 1950. Theory of mental tests. (1950).  Harold Gulliksen. 1950. Theory of mental tests. (1950).","DOI":"10.1037\/13240-000"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1353\/jhr.2012.0022"},{"key":"e_1_3_2_1_11_1","volume-title":"Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage.","author":"Hard Andrew","year":"2018","unstructured":"Andrew Hard , Kanishka Rao , Rajiv Mathews , Swaroop Ramaswamy , Francc oise Beaufays , Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018 . Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018). Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Francc oise Beaufays, Sean Augenstein, Hubert Eichner, Chlo\u00e9 Kiddon, and Daniel Ramage. 2018. Federated learning for mobile keyboard prediction. arXiv preprint arXiv:1811.03604 (2018)."},{"key":"e_1_3_2_1_12_1","volume-title":"LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data. arXiv preprint arXiv:1811.12629","author":"Huang Li","year":"2018","unstructured":"Li Huang , Yifeng Yin , Zeng Fu , Shifa Zhang , Hao Deng , and Dianbo Liu . 2018. LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data. arXiv preprint arXiv:1811.12629 ( 2018 ). Li Huang, Yifeng Yin, Zeng Fu, Shifa Zhang, Hao Deng, and Dianbo Liu. 2018. LoAdaBoost: Loss-Based AdaBoost Federated Machine Learning on medical Data. arXiv preprint arXiv:1811.12629 (2018)."},{"key":"e_1_3_2_1_13_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. 1352--1359.  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. 1352--1359.","DOI":"10.1609\/aaai.v31i1.10740"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3379507","article-title":"Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students","volume":"38","author":"Huang Zhenya","year":"2020","unstructured":"Zhenya Huang , Qi Liu , Yuying Chen , Le Wu , Keli Xiao , Enhong Chen , Haiping Ma , and Guoping Hu . 2020 . Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students . ACM Transactions on Information Systems (TOIS) , Vol. 38 , 2 (2020), 1 -- 33 . Zhenya Huang, Qi Liu, Yuying Chen, Le Wu, Keli Xiao, Enhong Chen, Haiping Ma, and Guoping Hu. 2020. Learning or Forgetting? A Dynamic Approach for Tracking the Knowledge Proficiency of Students. ACM Transactions on Information Systems (TOIS), Vol. 38, 2 (2020), 1--33.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_1_15_1","volume-title":"Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479","author":"Jeong Eunjeong","year":"2018","unstructured":"Eunjeong Jeong , Seungeun Oh , Hyesung Kim , Jihong Park , Mehdi Bennis , and Seong-Lyun Kim . 2018. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479 ( 2018 ). Eunjeong Jeong, Seungeun Oh, Hyesung Kim, Jihong Park, Mehdi Bennis, and Seong-Lyun Kim. 2018. Communication-efficient on-device machine learning: Federated distillation and augmentation under non-iid private data. arXiv preprint arXiv:1811.11479 (2018)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1109\/IJCNN.2019.8852464"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357909"},{"key":"e_1_3_2_1_18_1","volume-title":"Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al.","author":"Kairouz Peter","year":"2019","unstructured":"Peter Kairouz , H Brendan McMahan , Brendan Avent , Aur\u00e9lien Bellet , Mehdi Bennis , Arjun Nitin Bhagoji , Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019 . Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019). Peter Kairouz, H Brendan McMahan, Brendan Avent, Aur\u00e9lien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al. 2019. Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977 (2019)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/3289600.3290968"},{"key":"e_1_3_2_1_20_1","volume-title":"New horizons in testing","author":"Gage Kingsbury G","unstructured":"G Gage Kingsbury and David J Weiss . 1983. A comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure . In New horizons in testing . Elsevier , 257--283. G Gage Kingsbury and David J Weiss. 1983. A comparison of IRT-based adaptive mastery testing and a sequential mastery testing procedure. In New horizons in testing. Elsevier, 257--283."},{"key":"e_1_3_2_1_21_1","volume-title":"Piecing together the student success puzzle: research, propositions, and recommendations: ASHE Higher Education Report","author":"Kuh George D","unstructured":"George D Kuh , Jillian Kinzie , Jennifer A Buckley , Brian K Bridges , and John C Hayek . 2011. Piecing together the student success puzzle: research, propositions, and recommendations: ASHE Higher Education Report . Vol. 116 . John Wiley & Sons . George D Kuh, Jillian Kinzie, Jennifer A Buckley, Brian K Bridges, and John C Hayek. 2011. Piecing together the student success puzzle: research, propositions, and recommendations: ASHE Higher Education Report. Vol. 116. John Wiley & Sons."},{"key":"e_1_3_2_1_22_1","volume-title":"Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith.","author":"Li Tian","year":"2018","unstructured":"Tian Li , Anit Kumar Sahu , Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018 . Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018). Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. 2018. Federated optimization in heterogeneous networks. arXiv preprint arXiv:1812.06127 (2018)."},{"key":"e_1_3_2_1_23_1","volume-title":"On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189","author":"Li Xiang","year":"2019","unstructured":"Xiang Li , Kaixuan Huang , Wenhao Yang , Shusen Wang , and Zhihua Zhang . 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 ( 2019 ). Xiang Li, Kaixuan Huang, Wenhao Yang, Shusen Wang, and Zhihua Zhang. 2019. On the convergence of fedavg on non-iid data. arXiv preprint arXiv:1907.02189 (2019)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMCB.2011.2163711"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2011.118"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2924374"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3168361","article-title":"Fuzzy cognitive diagnosis for modelling examinee performance","volume":"9","author":"Liu Qi","year":"2018","unstructured":"Qi Liu , Runze Wu , Enhong Chen , Guandong Xu , Yu Su , Zhigang Chen , and Guoping Hu . 2018 . Fuzzy cognitive diagnosis for modelling examinee performance . ACM Transactions on Intelligent Systems and Technology (TIST) , Vol. 9 , 4 (2018), 1 -- 26 . Qi Liu, Runze Wu, Enhong Chen, Guandong Xu, Yu Su, Zhigang Chen, and Guoping Hu. 2018. Fuzzy cognitive diagnosis for modelling examinee performance. ACM Transactions on Intelligent Systems and Technology (TIST), Vol. 9, 4 (2018), 1--26.","journal-title":"ACM Transactions on Intelligent Systems and Technology (TIST)"},{"key":"e_1_3_2_1_28_1","unstructured":"FM Lord MR Novick and Allan Birnbaum. 1968. Statistical theories of mental test scores. (1968).  FM Lord MR Novick and Allan Birnbaum. 1968. Statistical theories of mental test scores. (1968)."},{"key":"e_1_3_2_1_29_1","volume-title":"Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619","author":"Mansour Yishay","year":"2020","unstructured":"Yishay Mansour , Mehryar Mohri , Jae Ro , and Ananda Theertha Suresh . 2020. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 ( 2020 ). Yishay Mansour, Mehryar Mohri, Jae Ro, and Ananda Theertha Suresh. 2020. Three approaches for personalization with applications to federated learning. arXiv preprint arXiv:2002.10619 (2020)."},{"key":"e_1_3_2_1_30_1","unstructured":"H Brendan McMahan Eider Moore Daniel Ramage Seth Hampson etal 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016).  H Brendan McMahan Eider Moore Daniel Ramage Seth Hampson et al. 2016. Communication-efficient learning of deep networks from decentralized data. arXiv preprint arXiv:1602.05629 (2016)."},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3303772.3303830"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-014-0007-7"},{"key":"e_1_3_2_1_33_1","volume-title":"Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. In 2019 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI). IEEE, 156--163","author":"Nakagawa Hiromi","year":"2019","unstructured":"Hiromi Nakagawa , Yusuke Iwasawa , and Yutaka Matsuo . 2019 . Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. In 2019 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI). IEEE, 156--163 . Hiromi Nakagawa, Yusuke Iwasawa, and Yutaka Matsuo. 2019. Graph-based Knowledge Tracing: Modeling Student Proficiency Using Graph Neural Network. In 2019 IEEE\/WIC\/ACM International Conference on Web Intelligence (WI). IEEE, 156--163."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/2021855.2021877"},{"key":"e_1_3_2_1_35_1","unstructured":"Chris Piech Jonathan Bassen Jonathan Huang Surya Ganguli Mehran Sahami Leonidas J Guibas and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in neural information processing systems. 505--513.  Chris Piech Jonathan Bassen Jonathan Huang Surya Ganguli Mehran Sahami Leonidas J Guibas and Jascha Sohl-Dickstein. 2015. Deep knowledge tracing. In Advances in neural information processing systems. 505--513."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1745-3984.1968.tb00642.x"},{"key":"e_1_3_2_1_37_1","volume-title":"Robust and communication-efficient federated learning from non-iid data","author":"Sattler Felix","year":"2019","unstructured":"Felix Sattler , Simon Wiedemann , Klaus-Robert M\u00fcller , and Wojciech Samek . 2019. Robust and communication-efficient federated learning from non-iid data . IEEE transactions on neural networks and learning systems ( 2019 ). Felix Sattler, Simon Wiedemann, Klaus-Robert M\u00fcller, and Wojciech Samek. 2019. Robust and communication-efficient federated learning from non-iid data. IEEE transactions on neural networks and learning systems (2019)."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11162-010-9202-3"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.6080"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330931"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/3051457.3053985"},{"key":"e_1_3_2_1_42_1","volume-title":"Eric G Van Inwegen, and Joseph E Beck","author":"Xiong Xiaolu","year":"2016","unstructured":"Xiaolu Xiong , Siyuan Zhao , Eric G Van Inwegen, and Joseph E Beck . 2016 . Going deeper with deep knowledge tracing. International Educational Data Mining Society ( 2016). Xiaolu Xiong, Siyuan Zhao, Eric G Van Inwegen, and Joseph E Beck. 2016. Going deeper with deep knowledge tracing. International Educational Data Mining Society (2016)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_1_44_1","volume-title":"Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903","author":"Yang Timothy","year":"2018","unstructured":"Timothy Yang , Galen Andrew , Hubert Eichner , Haicheng Sun , Wei Li , Nicholas Kong , Daniel Ramage , and Francc oise Beaufays . 2018. Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903 ( 2018 ). Timothy Yang, Galen Andrew, Hubert Eichner, Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, and Francc oise Beaufays. 2018. Applied federated learning: Improving google keyboard query suggestions. arXiv preprint arXiv:1812.02903 (2018)."},{"key":"e_1_3_2_1_45_1","volume-title":"FFD: A Federated Learning Based Method for Credit Card Fraud Detection. In International Conference on Big Data. Springer, 18--32","author":"Yang Wensi","year":"2019","unstructured":"Wensi Yang , Yuhang Zhang , Kejiang Ye , Li Li , and Cheng-Zhong Xu . 2019 b . FFD: A Federated Learning Based Method for Credit Card Fraud Detection. In International Conference on Big Data. Springer, 18--32 . Wensi Yang, Yuhang Zhang, Kejiang Ye, Li Li, and Cheng-Zhong Xu. 2019 b. FFD: A Federated Learning Based Method for Credit Card Fraud Detection. In International Conference on Big Data. Springer, 18--32."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/3231644.3231647"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-39112-5_18"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3038912.3052580"},{"key":"e_1_3_2_1_49_1","volume-title":"Federated learning with non-iid data. arXiv preprint arXiv:1806.00582","author":"Zhao Yue","year":"2018","unstructured":"Yue Zhao , Meng Li , Liangzhen Lai , Naveen Suda , Damon Civin , and Vikas Chandra . 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 ( 2018 ). Yue Zhao, Meng Li, Liangzhen Lai, Naveen Suda, Damon Civin, and Vikas Chandra. 2018. Federated learning with non-iid data. arXiv preprint arXiv:1806.00582 (2018)."}],"event":{"name":"WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event Israel","acronym":"WSDM '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 14th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441747","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3437963.3441747","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:35Z","timestamp":1750193255000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":49,"alternative-id":["10.1145\/3437963.3441747","10.1145\/3437963"],"URL":"https:\/\/doi.org\/10.1145\/3437963.3441747","relation":{},"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"2021-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}