{"id":"https://openalex.org/W4393407987","doi":"https://doi.org/10.3233/ida-230557","title":"Online course evaluation model based on graph auto-encoder","display_name":"Online course evaluation model based on graph auto-encoder","publication_year":2024,"publication_date":"2024-04-02","ids":{"openalex":"https://openalex.org/W4393407987","doi":"https://doi.org/10.3233/ida-230557"},"language":"en","primary_location":{"id":"doi:10.3233/ida-230557","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-230557","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","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/A5034840625","display_name":"Wei Yuan","orcid":"https://orcid.org/0000-0002-1370-0079"},"institutions":[{"id":"https://openalex.org/I161222507","display_name":"Open University of China","ror":"https://ror.org/02maxav80","country_code":"CN","type":"education","lineage":["https://openalex.org/I161222507"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wei Yuan","raw_affiliation_strings":["School of Computer Science, The Open University of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The Open University of China, Beijing, China","institution_ids":["https://openalex.org/I161222507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052346042","display_name":"Shiyu Zhao","orcid":"https://orcid.org/0000-0003-3098-8059"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyu Zhao","raw_affiliation_strings":["Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100336161","display_name":"Li Wang","orcid":"https://orcid.org/0000-0003-0510-7985"},"institutions":[{"id":"https://openalex.org/I161222507","display_name":"Open University of China","ror":"https://ror.org/02maxav80","country_code":"CN","type":"education","lineage":["https://openalex.org/I161222507"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Li Wang","raw_affiliation_strings":["School of Computer Science, The Open University of China, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, The Open University of China, Beijing, China","institution_ids":["https://openalex.org/I161222507"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006274477","display_name":"Lijia Cai","orcid":null},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lijia Cai","raw_affiliation_strings":["Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5070956153","display_name":"Yong Zhang","orcid":"https://orcid.org/0000-0001-6650-6790"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yong Zhang","raw_affiliation_strings":["Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing Key Laboratory of multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Department of Information Science, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5070956153"],"corresponding_institution_ids":["https://openalex.org/I37796252"],"apc_list":null,"apc_paid":null,"fwci":1.114,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.82974982,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"28","issue":"6","first_page":"1467","last_page":"1489"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9994000196456909,"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"}},"topics":[{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9994000196456909,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9894999861717224,"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/T10064","display_name":"Complex Network Analysis Techniques","score":0.9660999774932861,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7844566106796265},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.6648992896080017},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.57509845495224},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.5675422549247742},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5657336115837097},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5521702170372009},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.5386639833450317},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.4920770525932312},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4768182635307312},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4687020480632782},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4245002865791321},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3820435106754303},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15679171681404114}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7844566106796265},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.6648992896080017},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.57509845495224},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.5675422549247742},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5657336115837097},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5521702170372009},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.5386639833450317},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.4920770525932312},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4768182635307312},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4687020480632782},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4245002865791321},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3820435106754303},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15679171681404114},{"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/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3233/ida-230557","is_oa":false,"landing_page_url":"https://doi.org/10.3233/ida-230557","pdf_url":null,"source":{"id":"https://openalex.org/S2498839158","display_name":"Intelligent Data Analysis","issn_l":"1088-467X","issn":["1088-467X","1571-4128"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310318577","host_organization_name":"IOS Press","host_organization_lineage":["https://openalex.org/P4310318577"],"host_organization_lineage_names":["IOS Press"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Intelligent Data Analysis","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7300000190734863,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1966646122","https://openalex.org/W1976383685","https://openalex.org/W2026096677","https://openalex.org/W2069992838","https://openalex.org/W2100495367","https://openalex.org/W2140405352","https://openalex.org/W2186482614","https://openalex.org/W2335695885","https://openalex.org/W2335737022","https://openalex.org/W2529378427","https://openalex.org/W2543232345","https://openalex.org/W2557672990","https://openalex.org/W2618530766","https://openalex.org/W2740924709","https://openalex.org/W2756072049","https://openalex.org/W2770641296","https://openalex.org/W2788574423","https://openalex.org/W2788728386","https://openalex.org/W2804515444","https://openalex.org/W2897194906","https://openalex.org/W2905031825","https://openalex.org/W2971876272","https://openalex.org/W2972209102","https://openalex.org/W2972552529","https://openalex.org/W2985331920","https://openalex.org/W2994395295","https://openalex.org/W2995065126","https://openalex.org/W3012875109","https://openalex.org/W3022935508","https://openalex.org/W3031724472","https://openalex.org/W3035046120","https://openalex.org/W3094211122","https://openalex.org/W3124675547","https://openalex.org/W3195772876","https://openalex.org/W4228996929","https://openalex.org/W4286373861","https://openalex.org/W4307295769","https://openalex.org/W4311774127","https://openalex.org/W4312118664","https://openalex.org/W4313367679","https://openalex.org/W4385383230","https://openalex.org/W4386333391"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2566616303","https://openalex.org/W2159052453","https://openalex.org/W3131327266","https://openalex.org/W2734887215","https://openalex.org/W4297051394","https://openalex.org/W2669956259","https://openalex.org/W4249005693","https://openalex.org/W4392946183","https://openalex.org/W3088732000"],"abstract_inverted_index":{"In":[0,108,169,196],"the":[1,12,61,65,68,81,88,93,111,127,130,133,141,148,170,182,197],"post-epidemic":[2],"era,":[3],"online":[4,23,51,89,99,115,159],"learning":[5,70,83],"has":[6,33],"gained":[7],"increasing":[8],"attention":[9],"due":[10],"to":[11,21,76,120,125,139,186,201],"advancements":[13],"in":[14,181,217],"information":[15],"and":[16,37,63,85,154,193],"big":[17],"data":[18,25,31,62,117],"technology,":[19],"leading":[20],"large-scale":[22,69],"course":[24,52,100,116,160],"with":[26],"various":[27],"student":[28],"behaviors.":[29],"Online":[30],"mining":[32],"become":[34],"a":[35,105,176],"popular":[36],"important":[38],"way":[39],"of":[40,47,60,80,113,211],"extracting":[41],"valuable":[42],"insights":[43],"from":[44],"large":[45],"amounts":[46],"data.":[48,221],"However,":[49],"previous":[50],"analysis":[53],"methods":[54],"often":[55],"focused":[56],"on":[57,104,166],"individual":[58],"aspects":[59],"neglected":[64],"correlation":[66],"among":[67,129],"behavior":[71,84],"data,":[72],"which":[73],"can":[74],"lead":[75],"an":[77,98,207],"incomplete":[78],"understanding":[79],"overall":[82,208],"patterns":[86],"within":[87],"course.":[90],"To":[91],"solve":[92],"problems,":[94],"this":[95],"paper":[96],"proposes":[97],"evaluation":[101],"model":[102,205],"based":[103],"graph":[106,135],"auto-encoder.":[107],"our":[109,173,204],"method,":[110],"features":[112,151],"collected":[114],"are":[118],"used":[119],"construct":[121],"K-Nearest":[122],"Neighbor(KNN)":[123],"graphs":[124],"represent":[126],"association":[128],"courses.":[131],"Then":[132],"variational":[134],"auto-encoder(VGAE)":[136],"is":[137],"introduced":[138],"learn":[140],"useful":[142],"implicit":[143,150],"features.":[144],"Finally,":[145],"we":[146],"feed":[147],"learned":[149],"into":[152],"unsupervised":[153],"semi-supervised":[155],"downstream":[156],"tasks":[157],"for":[158],"evaluation,":[161],"respectively.":[162],"We":[163],"conduct":[164],"experiments":[165],"two":[167],"datasets.":[168],"clustering":[171],"task,":[172,199],"method":[174],"showed":[175],"more":[177],"than":[178],"tenfold":[179],"increase":[180],"Calinski-Harabasz":[183],"index":[184],"compared":[185,200],"unoptimized":[187],"features,":[188],"demonstrating":[189],"significant":[190],"structural":[191],"distinction":[192],"group":[194],"coherence.":[195],"classification":[198],"traditional":[202],"methods,":[203],"exhibited":[206],"performance":[209],"improvement":[210],"about":[212],"10%,":[213],"indicating":[214],"its":[215],"effectiveness":[216],"handling":[218],"complex":[219],"network":[220]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}
