{"id":"https://openalex.org/W3205662613","doi":"https://doi.org/10.1145/3474085.3475554","title":"Enhancing Knowledge Tracing via Adversarial Training","display_name":"Enhancing Knowledge Tracing via Adversarial Training","publication_year":2021,"publication_date":"2021-10-17","ids":{"openalex":"https://openalex.org/W3205662613","doi":"https://doi.org/10.1145/3474085.3475554","mag":"3205662613"},"language":"en","primary_location":{"id":"doi:10.1145/3474085.3475554","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475554","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5014286651","display_name":"Xiaopeng Guo","orcid":"https://orcid.org/0000-0003-1111-2035"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaopeng Guo","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100757960","display_name":"Zhijie Huang","orcid":"https://orcid.org/0000-0002-2019-3351"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijie Huang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064512555","display_name":"Jie Gao","orcid":"https://orcid.org/0000-0002-6970-727X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Gao","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5013254226","display_name":"Mingyu Shang","orcid":"https://orcid.org/0000-0003-3677-107X"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mingyu Shang","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066003557","display_name":"Maojing Shu","orcid":"https://orcid.org/0000-0002-5546-0114"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maojing Shu","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101761197","display_name":"Jun Sun","orcid":"https://orcid.org/0000-0001-6864-0803"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Sun","raw_affiliation_strings":["Peking University, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":6.8554,"has_fulltext":false,"cited_by_count":89,"citation_normalized_percentile":{"value":0.97386981,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"367","last_page":"375"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.9966999888420105,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9790999889373779,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9695000052452087,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8162875175476074},{"id":"https://openalex.org/keywords/adversarial-system","display_name":"Adversarial system","score":0.79414963722229},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.7156274318695068},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.7119858264923096},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6866304874420166},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6125244498252869},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6004494428634644},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5768886208534241},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5124583840370178},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.4648783206939697},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4577059745788574},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.42023777961730957},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35127130150794983},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.06950807571411133}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8162875175476074},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.79414963722229},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.7156274318695068},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.7119858264923096},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6866304874420166},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6125244498252869},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6004494428634644},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5768886208534241},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5124583840370178},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.4648783206939697},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4577059745788574},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.42023777961730957},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35127130150794983},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.06950807571411133},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3474085.3475554","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3474085.3475554","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 29th ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2967156844","display_name":"\u591a\u805a\u7126\u89c6\u9891\u7684\u878d\u5408\u4e0e\u7f16\u7801\u7814\u7a76","funder_award_id":"62071014","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W9657784","https://openalex.org/W160448112","https://openalex.org/W973843712","https://openalex.org/W1562092080","https://openalex.org/W1597703949","https://openalex.org/W1959691478","https://openalex.org/W2016043834","https://openalex.org/W2038192366","https://openalex.org/W2051963836","https://openalex.org/W2064675550","https://openalex.org/W2146856593","https://openalex.org/W2559094423","https://openalex.org/W2603824136","https://openalex.org/W2606234010","https://openalex.org/W2760600531","https://openalex.org/W2805035427","https://openalex.org/W2890071599","https://openalex.org/W2913933519","https://openalex.org/W2916628745","https://openalex.org/W2950651087","https://openalex.org/W2955931418","https://openalex.org/W2962907114","https://openalex.org/W2964185534","https://openalex.org/W2997645422","https://openalex.org/W3035009610","https://openalex.org/W3043869244","https://openalex.org/W3082341085","https://openalex.org/W3092100739","https://openalex.org/W3095721108","https://openalex.org/W3102281445","https://openalex.org/W3103836116"],"related_works":["https://openalex.org/W1574414179","https://openalex.org/W4362597605","https://openalex.org/W2115308562","https://openalex.org/W2789571330","https://openalex.org/W4299652732","https://openalex.org/W2963744171","https://openalex.org/W2362901774","https://openalex.org/W2133304975","https://openalex.org/W4248091533","https://openalex.org/W1515178292"],"abstract_inverted_index":{"We":[0],"study":[1],"the":[2,9,14,31,52,61,68,85,109,123,141,153,160,163,181,203],"problem":[3],"of":[4,35,54,70,111,205],"knowledge":[5,16,186,197,207],"tracing":[6],"(KT)":[7],"where":[8,180],"goal":[10],"is":[11,146,183,232],"to":[12,22,30,50,77,101,138,149,152,158,210],"trace":[13],"students'":[15],"mastery":[17],"over":[18,162],"time":[19],"so":[20],"as":[21,127,177],"make":[23,211],"predictions":[24],"on":[25,42,47,73,122,218],"their":[26],"future":[27],"performance.":[28,230],"Owing":[29],"good":[32],"representation":[33],"capacity":[34],"deep":[36],"neural":[37],"networks":[38],"(DNNs),":[39],"recent":[40],"advances":[41,87],"KT":[43,64,98,103,142,178],"have":[44],"increasingly":[45],"concentrated":[46],"exploring":[48],"DNNs":[49,62],"improve":[51],"performance":[53],"KT.":[55,112],"However,":[56],"we":[57,92,114,170],"empirically":[58],"reveal":[59],"that":[60,191,224],"based":[63,97],"models":[65],"may":[66],"run":[67],"risk":[69],"overfitting,":[71],"especially":[72],"small":[74],"datasets,":[75],"leading":[76],"limited":[78],"generalization.":[79],"In":[80],"this":[81],"paper,":[82],"by":[83],"leveraging":[84],"current":[86,206],"in":[88],"adversarial":[89,117,128,133,154],"training":[90],"(AT),":[91],"propose":[93],"an":[94,173],"efficient":[95,174],"AT":[96],"method":[99],"(ATKT)":[100],"enhance":[102,159],"model's":[104],"generalization":[105,161],"and":[106,119,132],"thus":[107],"push":[108],"limit":[110],"Specifically,":[113],"first":[115],"construct":[116],"perturbations":[118],"add":[120],"them":[121],"original":[124,131,164],"interaction":[125],"embeddings":[126],"examples.":[129],"The":[130],"examples":[134],"are":[135],"further":[136],"used":[137],"jointly":[139],"train":[140],"model,":[143],"forcing":[144],"it":[145],"not":[147],"only":[148],"be":[150],"robust":[151],"examples,":[155],"but":[156],"also":[157],"ones.":[165],"To":[166],"better":[167],"implement":[168],"AT,":[169],"then":[171],"present":[172],"attentive-LSTM":[175],"model":[176],"backbone,":[179],"key":[182],"a":[184,212],"proposed":[185],"hidden":[187,198,208],"state":[188,209],"attention":[189],"module":[190],"adaptively":[192],"aggregates":[193],"information":[194],"from":[195],"previous":[196],"states":[199],"while":[200],"simultaneously":[201],"highlighting":[202],"importance":[204],"more":[213],"accurate":[214],"prediction.":[215],"Extensive":[216],"experiments":[217],"four":[219],"public":[220],"benchmark":[221],"datasets":[222],"demonstrate":[223],"our":[225],"ATKT":[226],"achieves":[227],"new":[228],"state-of-the-art":[229],"Code":[231],"available":[233],"at:":[234],"https://github.com/xiaopengguo/ATKT.":[235]},"counts_by_year":[{"year":2026,"cited_by_count":10},{"year":2025,"cited_by_count":30},{"year":2024,"cited_by_count":30},{"year":2023,"cited_by_count":14},{"year":2022,"cited_by_count":5}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}