{"id":"https://openalex.org/W4385453179","doi":"https://doi.org/10.1109/access.2023.3300703","title":"Graph Attention Neural Network Model With Behavior Features for Knowledge Tracking","display_name":"Graph Attention Neural Network Model With Behavior Features for Knowledge Tracking","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W4385453179","doi":"https://doi.org/10.1109/access.2023.3300703"},"language":"en","primary_location":{"id":"doi:10.1109/access.2023.3300703","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3300703","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10198404.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10198404.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100441830","display_name":"Wei Zhang","orcid":"https://orcid.org/0009-0008-8218-753X"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Zhang","raw_affiliation_strings":["Department of Artificial Intelligence Education, Central China Normal University, Wuhan, China","National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Education, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]},{"raw_affiliation_string":"National Engineering Laboratory for Educational Big Data, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102753471","display_name":"Sen Hu","orcid":"https://orcid.org/0000-0003-1507-0215"},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sen Hu","raw_affiliation_strings":["Department of Artificial Intelligence Education, Central China Normal University, Wuhan, China"],"raw_orcid":"https://orcid.org/0000-0003-1507-0215","affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Education, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064807509","display_name":"Kaiyuan Qu","orcid":null},"institutions":[{"id":"https://openalex.org/I40963666","display_name":"Central China Normal University","ror":"https://ror.org/03x1jna21","country_code":"CN","type":"education","lineage":["https://openalex.org/I40963666"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kaiyuan Qu","raw_affiliation_strings":["Department of Artificial Intelligence Education, Central China Normal University, Wuhan, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Artificial Intelligence Education, Central China Normal University, Wuhan, China","institution_ids":["https://openalex.org/I40963666"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I40963666"],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":1.4517,"has_fulltext":true,"cited_by_count":9,"citation_normalized_percentile":{"value":0.85433369,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"11","issue":null,"first_page":"88329","last_page":"88338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9994999766349792,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.9994999766349792,"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/T11122","display_name":"Online Learning and Analytics","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1706","display_name":"Computer Science Applications"},"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/T10636","display_name":"Innovative Teaching and Learning Methods","score":0.9864000082015991,"subfield":{"id":"https://openalex.org/subfields/3204","display_name":"Developmental and Educational Psychology"},"field":{"id":"https://openalex.org/fields/32","display_name":"Psychology"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7446641325950623},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6515960693359375},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5781862735748291},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5676195025444031},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5598254203796387},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4901476800441742},{"id":"https://openalex.org/keywords/matrix-representation","display_name":"Matrix representation","score":0.4281185269355774},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.426244854927063},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4219900071620941},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.19941559433937073}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7446641325950623},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6515960693359375},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5781862735748291},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5676195025444031},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5598254203796387},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4901476800441742},{"id":"https://openalex.org/C103275481","wikidata":"https://www.wikidata.org/wiki/Q6787889","display_name":"Matrix representation","level":3,"score":0.4281185269355774},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.426244854927063},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4219900071620941},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.19941559433937073},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C2781311116","wikidata":"https://www.wikidata.org/wiki/Q83306","display_name":"Group (periodic table)","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"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/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2023.3300703","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3300703","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10198404.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:57a4a3df4087426b836a8da262cda90b","is_oa":true,"landing_page_url":"https://doaj.org/article/57a4a3df4087426b836a8da262cda90b","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 11, Pp 88329-88338 (2023)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2023.3300703","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2023.3300703","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/10198404.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4","score":0.7699999809265137}],"awards":[{"id":"https://openalex.org/G5095216809","display_name":null,"funder_award_id":"61977031","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":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4385453179.pdf","grobid_xml":"https://content.openalex.org/works/W4385453179.grobid-xml"},"referenced_works_count":33,"referenced_works":["https://openalex.org/W2015040676","https://openalex.org/W2041146801","https://openalex.org/W2132914434","https://openalex.org/W2559094423","https://openalex.org/W2606234010","https://openalex.org/W2771404308","https://openalex.org/W2805516822","https://openalex.org/W2883043540","https://openalex.org/W2913933519","https://openalex.org/W2941898411","https://openalex.org/W2957747000","https://openalex.org/W2963625192","https://openalex.org/W2980472839","https://openalex.org/W3000394975","https://openalex.org/W3034854756","https://openalex.org/W3035442578","https://openalex.org/W3095602948","https://openalex.org/W3107452283","https://openalex.org/W3127790743","https://openalex.org/W3132748888","https://openalex.org/W3134371412","https://openalex.org/W3197356758","https://openalex.org/W3197400233","https://openalex.org/W3200290995","https://openalex.org/W4285606137","https://openalex.org/W4297733535","https://openalex.org/W6751796012","https://openalex.org/W6762564823","https://openalex.org/W6765830420","https://openalex.org/W6772902609","https://openalex.org/W6778556382","https://openalex.org/W6783313556","https://openalex.org/W6784694379"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W2611989081","https://openalex.org/W2731899572","https://openalex.org/W4230611425","https://openalex.org/W2062195135","https://openalex.org/W4294635752","https://openalex.org/W4304166257","https://openalex.org/W4383066092","https://openalex.org/W2006221579","https://openalex.org/W2157322189"],"abstract_inverted_index":{"The":[0],"existing":[1,175],"deep":[2],"knowledge":[3,71,177],"tracking":[4,72,178],"models":[5,27],"ignore":[6],"the":[7,20,26,30,37,48,52,58,101],"high-order":[8],"relationship":[9,116],"between":[10,39,125],"students\u2019":[11,40,97,143],"behavior":[12,68,85,93,98],"features":[13,69,99],"and":[14,36,44,104,119,127,140,157,171,180],"questions":[15,102,108,126],"with":[16,67,174],"overlapping":[17],"skills":[18],"in":[19,76,166],"learning":[21,32,84,92],"process.":[22],"As":[23],"a":[24,61,82,91,106,114,121],"result,":[25],"cannot":[28],"learn":[29],"complete":[31],"track":[33],"of":[34,51,123],"students":[35],"dependence":[38],"historical":[41],"answer":[42],"records":[43],"questions,":[45,128],"which":[46],"affect":[47],"predictive":[49],"performance":[50],"model.":[53],"In":[54],"order":[55],"to":[56,135],"solve":[57],"above":[59],"problems,":[60],"graph":[62,122,130],"attention":[63,131],"neural":[64,132],"network":[65,133],"model":[66],"for":[70],"(GAKT-BF)":[73],"is":[74],"proposed":[75],"this":[77],"paper.":[78],"Firstly,":[79],"GAKT-BF":[80,112,162],"designs":[81,105,113],"student":[83],"feature":[86,94],"information":[87],"extraction":[88,117],"module,":[89],"constructs":[90,120],"matrix,":[95],"incorporates":[96],"into":[100],"representation,":[103],"new":[107],"representation":[109],"method.":[110],"Then,":[111],"question":[115,137],"module":[118],"correlations":[124],"uses":[129],"(GAT)":[134],"extract":[136],"vector":[138],"representations,":[139],"finally":[141],"predicts":[142],"next":[144],"answers":[145],"through":[146],"long":[147],"short-term":[148],"memory":[149],"(LSTM).":[150],"Experiments":[151],"on":[152],"Assistments":[153],"2009,":[154],"KDD":[155],"CUP":[156],"Assistment-17":[158],"datasets":[159],"show":[160],"that":[161],"has":[163,181],"significantly":[164],"improved":[165],"both":[167],"evaluation":[168],"metrics":[169],"AUC":[170],"ACC":[172],"compared":[173],"advanced":[176],"models,":[179],"better":[182],"prediction":[183],"results.":[184]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}