{"id":"https://openalex.org/W4328007265","doi":"https://doi.org/10.1109/tlt.2023.3259013","title":"Improving Knowledge Tracing via Considering Two Types of Actual Differences From Exercises and Prior Knowledge","display_name":"Improving Knowledge Tracing via Considering Two Types of Actual Differences From Exercises and Prior Knowledge","publication_year":2023,"publication_date":"2023-03-20","ids":{"openalex":"https://openalex.org/W4328007265","doi":"https://doi.org/10.1109/tlt.2023.3259013"},"language":"en","primary_location":{"id":"doi:10.1109/tlt.2023.3259013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tlt.2023.3259013","pdf_url":null,"source":{"id":"https://openalex.org/S130363450","display_name":"IEEE Transactions on Learning Technologies","issn_l":"1939-1382","issn":["1939-1382","2372-0050"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Learning Technologies","raw_type":"journal-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/A5006169810","display_name":"Shun Mao","orcid":"https://orcid.org/0000-0001-6625-2348"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shun Mao","raw_affiliation_strings":["Department the School of Computer Science, South China Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department the School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101418960","display_name":"Jieyu Zhan","orcid":"https://orcid.org/0000-0001-7403-1086"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jieyu Zhan","raw_affiliation_strings":["Department the School of Computer Science, South China Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department the School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062123619","display_name":"Yizhao Wang","orcid":"https://orcid.org/0000-0002-2027-1495"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yizhao Wang","raw_affiliation_strings":["Department the School of Computer Science, South China Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Department the School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030884785","display_name":"Yuncheng Jiang","orcid":"https://orcid.org/0000-0002-0402-5382"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuncheng Jiang","raw_affiliation_strings":["School of Computer Science, South China Normal University, Guangzhou, China","School of Artificial Intelligence, South China Normal University, Foshan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]},{"raw_affiliation_string":"School of Artificial Intelligence, South China Normal University, Foshan, China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5006169810"],"corresponding_institution_ids":["https://openalex.org/I187400657"],"apc_list":null,"apc_paid":null,"fwci":5.2448,"has_fulltext":false,"cited_by_count":31,"citation_normalized_percentile":{"value":0.96483298,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"16","issue":"3","first_page":"324","last_page":"338"},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.991100013256073,"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/T10028","display_name":"Topic Modeling","score":0.9736999869346619,"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":0.806659460067749},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.7350606918334961},{"id":"https://openalex.org/keywords/tracing","display_name":"Tracing","score":0.6562734842300415},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5589957237243652},{"id":"https://openalex.org/keywords/relevance","display_name":"Relevance (law)","score":0.5429785251617432},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5021321773529053}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.806659460067749},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7350606918334961},{"id":"https://openalex.org/C138673069","wikidata":"https://www.wikidata.org/wiki/Q322229","display_name":"Tracing","level":2,"score":0.6562734842300415},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5589957237243652},{"id":"https://openalex.org/C158154518","wikidata":"https://www.wikidata.org/wiki/Q7310970","display_name":"Relevance (law)","level":2,"score":0.5429785251617432},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5021321773529053},{"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/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"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/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tlt.2023.3259013","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tlt.2023.3259013","pdf_url":null,"source":{"id":"https://openalex.org/S130363450","display_name":"IEEE Transactions on Learning Technologies","issn_l":"1939-1382","issn":["1939-1382","2372-0050"],"is_oa":false,"is_in_doaj":false,"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 Transactions on Learning Technologies","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.8199999928474426,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2202315836","display_name":null,"funder_award_id":"U1911201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4171216190","display_name":null,"funder_award_id":"61772210","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":58,"referenced_works":["https://openalex.org/W650350307","https://openalex.org/W1562092080","https://openalex.org/W1597703949","https://openalex.org/W1959691478","https://openalex.org/W2015040676","https://openalex.org/W2016589492","https://openalex.org/W2093135704","https://openalex.org/W2143612262","https://openalex.org/W2187089797","https://openalex.org/W2338445680","https://openalex.org/W2514897959","https://openalex.org/W2559094423","https://openalex.org/W2574518178","https://openalex.org/W2603824136","https://openalex.org/W2615786590","https://openalex.org/W2788574423","https://openalex.org/W2805035427","https://openalex.org/W2883150817","https://openalex.org/W2907913484","https://openalex.org/W2913933519","https://openalex.org/W2919115771","https://openalex.org/W2941898411","https://openalex.org/W2953577593","https://openalex.org/W2955931418","https://openalex.org/W2957747000","https://openalex.org/W2963448850","https://openalex.org/W2964195932","https://openalex.org/W2965671729","https://openalex.org/W2966026442","https://openalex.org/W2966684417","https://openalex.org/W2966856421","https://openalex.org/W2970480615","https://openalex.org/W2979826702","https://openalex.org/W2980472839","https://openalex.org/W3011937962","https://openalex.org/W3046352389","https://openalex.org/W3082311223","https://openalex.org/W3082341085","https://openalex.org/W3092100739","https://openalex.org/W3102281445","https://openalex.org/W3165396427","https://openalex.org/W3197376746","https://openalex.org/W4206706211","https://openalex.org/W4230150613","https://openalex.org/W4294170691","https://openalex.org/W4307561542","https://openalex.org/W4385245566","https://openalex.org/W6621483976","https://openalex.org/W6682691769","https://openalex.org/W6703457600","https://openalex.org/W6704546155","https://openalex.org/W6732172084","https://openalex.org/W6739901393","https://openalex.org/W6762564823","https://openalex.org/W6765830420","https://openalex.org/W6766512362","https://openalex.org/W6766552247","https://openalex.org/W6766582345"],"related_works":["https://openalex.org/W2961085424","https://openalex.org/W4306674287","https://openalex.org/W3046775127","https://openalex.org/W3107602296","https://openalex.org/W4394896187","https://openalex.org/W3170094116","https://openalex.org/W4386462264","https://openalex.org/W4364306694","https://openalex.org/W4312192474","https://openalex.org/W4283697347"],"abstract_inverted_index":{"For":[0],"offering":[1],"adaptive":[2],"learning":[3,24],"to":[4,21,49,66,71,109,131,163,173,203],"learners":[5],"in":[6,38,90,205],"intelligent":[7],"tutoring":[8],"systems,":[9],"one":[10],"of":[11,54,98,102,114,136],"the":[12,44,59,67,73,77,82,87,103,107,111,142,147,165,181,209],"fundamental":[13],"tasks":[14],"is":[15,93,161],"knowledge":[16,45,92,124,149],"tracing":[17,125],"(KT),":[18],"which":[19,64],"aims":[20],"assess":[22],"learners'":[23,51],"states":[25],"and":[26,151,170],"make":[27],"prediction":[28,112,206],"for":[29],"future":[30],"performance.":[31,85],"However,":[32],"there":[33],"are":[34,47],"two":[35,100,134],"crucial":[36],"issues":[37],"deep":[39],"learning-based":[40],"KT":[41,68,83,115,199],"models.":[42],"First,":[43],"concepts":[46,150],"used":[48],"predict":[50],"performance":[52,113,207],"instead":[53],"exercises.":[55,155],"This":[56],"choice":[57],"ignores":[58],"actual":[60,88,104,137,152],"difference":[61,89,153],"among":[62,154],"exercises,":[63],"leads":[65],"model's":[69,84],"inability":[70],"explore":[72],"rich":[74],"information":[75],"at":[76],"exercise":[78,143],"level,":[79],"thus,":[80],"weakening":[81],"Second,":[86],"prior":[91,176],"neglected.":[94],"Therefore,":[95],"in-depth":[96],"research":[97],"these":[99,133],"types":[101,135],"differences":[105],"offers":[106],"possibility":[108],"improve":[110],"further.":[116],"To":[117],"this":[118],"end,":[119],"we":[120,179],"propose":[121],"a":[122],"fine-grained":[123],"model":[126,184,196],"(FGKT)":[127],"<sup":[128],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[129],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">1</sup>":[130],"capture":[132,164],"differences.":[138],"We":[139],"first":[140],"obtain":[141],"representations":[144],"by":[145,201],"considering":[146],"corresponding":[148],"Then,":[156],"an":[157],"effective":[158],"attention":[159],"mechanism":[160],"designed":[162],"relevance":[166],"between":[167],"assessment":[168],"exercises":[169],"historical":[171],"interactions":[172],"acquire":[174],"individual":[175],"knowledge.":[177],"Finally,":[178],"evaluate":[180],"proposed":[182],"FGKT":[183],"on":[185,208],"several":[186],"available":[187],"benchmark":[188,198],"datasets.":[189],"The":[190],"experiment":[191],"results":[192],"show":[193],"that":[194],"our":[195],"surpasses":[197],"models":[200],"up":[202],"7%":[204],"latest":[210],"ASSISTments":[211],"dataset.":[212]},"counts_by_year":[{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":14},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
