{"id":"https://openalex.org/W7127309531","doi":"https://doi.org/10.1109/tlt.2026.3660059","title":"Integrating Blended Learning Behaviors via Multimodal Fusion for Student Performance Prediction","display_name":"Integrating Blended Learning Behaviors via Multimodal Fusion for Student Performance Prediction","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7127309531","doi":"https://doi.org/10.1109/tlt.2026.3660059"},"language":null,"primary_location":{"id":"doi:10.1109/tlt.2026.3660059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tlt.2026.3660059","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/A5101545018","display_name":"Han Wan","orcid":"https://orcid.org/0000-0002-9238-451X"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Han Wan","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-9238-451X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106752626","display_name":"Shiyang Yue","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shiyang Yue","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-3119-319X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124884778","display_name":"Mengying Li","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Mengying Li","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0000-9860-4248","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124931368","display_name":"Xin Luo","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Luo","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0002-9314-5147","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124946078","display_name":"Yaofeng Hu","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaofeng Hu","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0006-3351-0383","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062092279","display_name":"Baoliang Che","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoliang Che","raw_affiliation_strings":["State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0009-0004-2484-1530","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124934621","display_name":"Jingyuan Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingyuan Wang","raw_affiliation_strings":["School of Computer Science and Engineering, Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-0651-1592","affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5101545018"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.21999174,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"19","issue":null,"first_page":"87","last_page":"104"},"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":0.4970000088214874,"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":0.4970000088214874,"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":0.2946000099182129,"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/T10667","display_name":"Emotion and Mood Recognition","score":0.00989999994635582,"subfield":{"id":"https://openalex.org/subfields/3205","display_name":"Experimental and Cognitive 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/performance-prediction","display_name":"Performance prediction","score":0.4828999936580658},{"id":"https://openalex.org/keywords/multimodal-learning","display_name":"Multimodal learning","score":0.46700000762939453},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4059999883174896},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.40059998631477356},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.39309999346733093},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.3833000063896179},{"id":"https://openalex.org/keywords/track","display_name":"Track (disk drive)","score":0.37470000982284546},{"id":"https://openalex.org/keywords/blended-learning","display_name":"Blended learning","score":0.37040001153945923}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8277999758720398},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5583999752998352},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5249999761581421},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.4828999936580658},{"id":"https://openalex.org/C2780660688","wikidata":"https://www.wikidata.org/wiki/Q25052564","display_name":"Multimodal learning","level":2,"score":0.46700000762939453},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4059999883174896},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.40059998631477356},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.39309999346733093},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C89992363","wikidata":"https://www.wikidata.org/wiki/Q5961558","display_name":"Track (disk drive)","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C158592959","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Blended learning","level":3,"score":0.37040001153945923},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.36410000920295715},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.3621000051498413},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.35100001096725464},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.3370000123977661},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.3190000057220459},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C49774154","wikidata":"https://www.wikidata.org/wiki/Q131765","display_name":"Multimedia","level":1,"score":0.28189998865127563},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2685000002384186},{"id":"https://openalex.org/C183322885","wikidata":"https://www.wikidata.org/wiki/Q17007702","display_name":"Context model","level":3,"score":0.25589999556541443}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tlt.2026.3660059","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tlt.2026.3660059","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":53,"referenced_works":["https://openalex.org/W1539811621","https://openalex.org/W1678356000","https://openalex.org/W2032406331","https://openalex.org/W2530083052","https://openalex.org/W2611285220","https://openalex.org/W2624448409","https://openalex.org/W2794357746","https://openalex.org/W2802314367","https://openalex.org/W2889161747","https://openalex.org/W2929144106","https://openalex.org/W2933627812","https://openalex.org/W2982670714","https://openalex.org/W2995682556","https://openalex.org/W3000504755","https://openalex.org/W3003495087","https://openalex.org/W3039470340","https://openalex.org/W3044050414","https://openalex.org/W3046090708","https://openalex.org/W3049450989","https://openalex.org/W3106194696","https://openalex.org/W3113641224","https://openalex.org/W3118366136","https://openalex.org/W3127073259","https://openalex.org/W3128420506","https://openalex.org/W3175906877","https://openalex.org/W3205296805","https://openalex.org/W4214890135","https://openalex.org/W4225323780","https://openalex.org/W4290755875","https://openalex.org/W4296640294","https://openalex.org/W4308115534","https://openalex.org/W4312385964","https://openalex.org/W4312799843","https://openalex.org/W4320005506","https://openalex.org/W4321442120","https://openalex.org/W4376622421","https://openalex.org/W4381145042","https://openalex.org/W4385245566","https://openalex.org/W4385701215","https://openalex.org/W4385777586","https://openalex.org/W4385931952","https://openalex.org/W4387087093","https://openalex.org/W4391560474","https://openalex.org/W4393241334","https://openalex.org/W4394926451","https://openalex.org/W4400762160","https://openalex.org/W4402630814","https://openalex.org/W4403558234","https://openalex.org/W4404051441","https://openalex.org/W4405890982","https://openalex.org/W4408763002","https://openalex.org/W4409644306","https://openalex.org/W4410255710"],"related_works":[],"abstract_inverted_index":{"Blended":[0],"learning":[1,14,158,165],"enriches":[2],"students'":[3,29],"experiences":[4],"across":[5,45],"diverse":[6],"environments":[7],"while":[8],"generating":[9],"multimodal":[10,25,81,110,157],"data":[11,26],"related":[12],"to":[13,27,58,117],"activities.":[15],"However,":[16],"it":[17],"presents":[18],"challenges":[19],"in":[20],"the":[21,62,66,118,125,131,135,138,170],"appropriate":[22],"use":[23],"of":[24,128,140,145,173],"track":[28,162],"performance":[30,60],"development.":[31],"Previous":[32],"models":[33],"with":[34],"fixed-length":[35],"inputs":[36],"or":[37],"static":[38],"fusion":[39,82,111],"mechanisms":[40],"inadequately":[41],"model":[42,72,133],"temporal":[43,79],"dependencies":[44],"behavioral":[46],"modalities.":[47],"In":[48],"this":[49],"paper,":[50],"we":[51],"integrated":[52],"variable-length":[53],"time":[54],"series":[55],"over":[56],"weeks":[57],"forecast":[59],"for":[61,78],"subsequent":[63],"week.":[64],"As":[65],"main":[67],"contribution,":[68],"a":[69,76],"two-stage":[70],"training":[71],"that":[73,109],"relies":[74],"on":[75,88,147,151],"transformer":[77],"attention-based":[80],"was":[83],"proposed.":[84],"We":[85],"conducted":[86],"experiments":[87],"two":[89],"real-world":[90],"datasets,":[91],"FC2023":[92,148],"and":[93,102,149,176],"CS2023,":[94],"derived":[95],"from":[96],"hybrid":[97],"mode":[98],"courses":[99],"involving":[100],"439":[101],"199":[103],"students,":[104],"respectively.":[105],"The":[106],"results":[107],"demonstrate":[108],"yields":[112],"better":[113],"periodical":[114],"prediction":[115],"compared":[116],"unimodal":[119],"approach.":[120],"Ultimately,":[121],"aiming":[122],"at":[123],"predicting":[124],"week-by-week":[126],"development":[127],"student":[129],"performance,":[130],"proposed":[132],"achieves":[134],"Area":[136],"Under":[137],"Curve":[139],"Receiver":[141],"Operating":[142],"Characteristic":[143],"(ROC-AUC)":[144],"81.02%":[146],"82.65%":[150],"CS2023.":[152],"This":[153],"approach,":[154],"which":[155],"leverages":[156],"analytics,":[159],"helps":[160],"educators":[161],"each":[163],"student's":[164],"progress":[166],"more":[167],"effectively,":[168],"enabling":[169],"timely":[171],"implementation":[172],"instructional":[174],"interventions":[175],"enhancing":[177],"educational":[178],"outcomes.":[179]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2026-02-04T00:00:00"}