{"id":"https://openalex.org/W7223671710","doi":"https://doi.org/10.1007/s44163-026-02002-8","title":"Mitigating domain shift in cross-domain student performance prediction using a CORAL-based adaptation framework","display_name":"Mitigating domain shift in cross-domain student performance prediction using a CORAL-based adaptation framework","publication_year":2026,"publication_date":"2026-10-26","ids":{"openalex":"https://openalex.org/W7223671710","doi":"https://doi.org/10.1007/s44163-026-02002-8"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-026-02002-8","doi":"https://doi.org/10.1007/s44163-026-02002-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02002-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02002-8.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl"],"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"volume":null,"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02002-8.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5039509523","display_name":"Achmad Bisri","orcid":"https://orcid.org/0000-0002-0426-253X","observed_orcids":["https://orcid.org/0000-0002-0426-253X"]},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]},{"id":"https://openalex.org/I4405253599","display_name":"Universitas Islam Negeri Sultan Maulana Hasanuddin Banten","ror":"https://ror.org/00vzpka39","country_code":"ID","type":"education","lineage":["https://openalex.org/I4405253599"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Achmad Bisri","raw_affiliation_strings":["Department of Informatics, Institut Teknologi Sepuluh Nopember, Sukolilo Street, Surabaya, 60111, Indonesia","Universitas Islam Negeri Sultan Maulana Hasanuddin Banten, Syech Nawawi Al-Bantani Street, Serang, 42171, Indonesia"],"raw_orcid":"https://orcid.org/0000-0002-0426-253X","affiliations":[{"raw_affiliation_string":"Department of Informatics, Institut Teknologi Sepuluh Nopember, Sukolilo Street, Surabaya, 60111, Indonesia","institution_ids":["https://openalex.org/I166843116"]},{"raw_affiliation_string":"Universitas Islam Negeri Sultan Maulana Hasanuddin Banten, Syech Nawawi Al-Bantani Street, Serang, 42171, Indonesia","institution_ids":["https://openalex.org/I4405253599"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019640260","display_name":"Umi Laili Yuhana","orcid":"https://orcid.org/0000-0001-5237-7457","observed_orcids":["https://orcid.org/0000-0001-5237-7457"]},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":true,"raw_author_name":"Umi Laili Yuhana","raw_affiliation_strings":["Department of Informatics, Institut Teknologi Sepuluh Nopember, Sukolilo Street, Surabaya, 60111, Indonesia"],"raw_orcid":"https://orcid.org/0000-0001-5237-7457","affiliations":[{"raw_affiliation_string":"Department of Informatics, Institut Teknologi Sepuluh Nopember, Sukolilo Street, Surabaya, 60111, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041186429","display_name":"Bilqis Amaliah","orcid":"https://orcid.org/0000-0003-3154-8653","observed_orcids":["https://orcid.org/0000-0003-3154-8653"]},"institutions":[{"id":"https://openalex.org/I166843116","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89","country_code":"ID","type":"education","lineage":["https://openalex.org/I166843116"]}],"countries":["ID"],"is_corresponding":false,"raw_author_name":"Bilqis Amaliah","raw_affiliation_strings":["Department of Informatics, Institut Teknologi Sepuluh Nopember, Sukolilo Street, Surabaya, 60111, Indonesia"],"raw_orcid":"https://orcid.org/0000-0003-3154-8653","affiliations":[{"raw_affiliation_string":"Department of Informatics, Institut Teknologi Sepuluh Nopember, Sukolilo Street, Surabaya, 60111, Indonesia","institution_ids":["https://openalex.org/I166843116"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089045516","display_name":"Misita Anwar","orcid":"https://orcid.org/0000-0003-3979-1746","observed_orcids":["https://orcid.org/0000-0003-3979-1746"]},"institutions":[{"id":"https://openalex.org/I57093077","display_name":"Swinburne University of Technology","ror":"https://ror.org/031rekg67","country_code":"AU","type":"education","lineage":["https://openalex.org/I57093077"]}],"countries":["AU"],"is_corresponding":false,"raw_author_name":"Misita Anwar","raw_affiliation_strings":["Swinburne University of Technology, John Street, Hawthorn VIC, 3122, Australia"],"raw_orcid":"https://orcid.org/0000-0003-3979-1746","affiliations":[{"raw_affiliation_string":"Swinburne University of Technology, John Street, Hawthorn VIC, 3122, Australia","institution_ids":["https://openalex.org/I57093077"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5019640260"],"corresponding_institution_ids":["https://openalex.org/I166843116"],"apc_list":{"value":1590,"currency":"USD","value_usd":1590},"apc_paid":{"value":1590,"currency":"USD","value_usd":1590},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.86819111,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"6","issue":"1","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11122","display_name":"Online Learning and Analytics","score":0.9976000189781189,"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":0.9976000189781189,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0017999999690800905,"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/T11902","display_name":"Intelligent Tutoring Systems and Adaptive Learning","score":0.00019999999494757503,"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/academic-performance-prediction","display_name":"student performance prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/cross-domain-learning","display_name":"cross-domain learning","score":0.6830000281333923},{"id":"https://openalex.org/keywords/domain-adaptation","display_name":"domain adaptation","score":0.9169999957084656},{"id":"https://openalex.org/keywords/coral","display_name":"coral","score":0.9459999799728394},{"id":"https://openalex.org/keywords/distribution-shift","display_name":"distribution shift","score":0.8859999775886536},{"id":"https://openalex.org/keywords/academic-programs","display_name":"academic programs","score":0.7580000162124634},{"id":"https://openalex.org/keywords/educational-data-mining","display_name":"educational data mining","score":0.9169999957084656},{"id":"https://openalex.org/keywords/model-generalization","display_name":"model generalization","score":0.6869999766349792}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.7398999929428101},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6822999715805054},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6244000196456909},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5702000260353088},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.5385000109672546},{"id":"https://openalex.org/C206041023","wikidata":"https://www.wikidata.org/wiki/Q1751970","display_name":"Wilcoxon signed-rank test","level":3,"score":0.5117999911308289},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4691999852657318},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.4677000045776367},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.43230000138282776},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.39309999346733093},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3921999931335449},{"id":"https://openalex.org/C16910744","wikidata":"https://www.wikidata.org/wiki/Q7705759","display_name":"Test data","level":2,"score":0.38690000772476196},{"id":"https://openalex.org/C2777115002","wikidata":"https://www.wikidata.org/wiki/Q7168246","display_name":"Performance prediction","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C178650346","wikidata":"https://www.wikidata.org/wiki/Q201984","display_name":"Covariance","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C2778915421","wikidata":"https://www.wikidata.org/wiki/Q3643177","display_name":"Performance improvement","level":2,"score":0.37059998512268066},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.36320000886917114},{"id":"https://openalex.org/C2776434776","wikidata":"https://www.wikidata.org/wiki/Q19246213","display_name":"Domain adaptation","level":3,"score":0.34200000762939453},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.3359000086784363},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C119340705","wikidata":"https://www.wikidata.org/wiki/Q1628597","display_name":"Analysis of covariance","level":2,"score":0.3084000051021576},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.2766000032424927},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C2777598771","wikidata":"https://www.wikidata.org/wiki/Q5341279","display_name":"Educational data mining","level":2,"score":0.26589998602867126},{"id":"https://openalex.org/C87007009","wikidata":"https://www.wikidata.org/wiki/Q210832","display_name":"Statistical hypothesis testing","level":2,"score":0.26499998569488525},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2508000135421753}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-026-02002-8","doi":"https://doi.org/10.1007/s44163-026-02002-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02002-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02002-8.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl"],"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9471cda96b014cad8a21ec89ceec3111","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/9471cda96b014cad8a21ec89ceec3111","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,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Discover Artificial Intelligence, Vol 6, Iss 1 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1007/s44163-026-02002-8","doi":"https://doi.org/10.1007/s44163-026-02002-8","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02002-8","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02002-8.pdf","source":{"id":"https://openalex.org/S4210220416","display_name":"Discover Artificial Intelligence","issn_l":"2731-0809","issn":["2731-0809"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","jufo-1","norway-1","oarl"],"host_organization":"https://openalex.org/P4310319965","host_organization_name":"Springer Nature","host_organization_lineage":["https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Discover Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.9235000014305115,"id":"https://metadata.un.org/sdg/4","display_name":"Quality education"}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320327937","display_name":"Institut Teknologi Sepuluh Nopember","ror":"https://ror.org/05kbmmt89"},{"id":"https://openalex.org/F4320328515","display_name":"Lembaga Pengelola Dana Pendidikan","ror":"https://ror.org/05p2xef58"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7223671710.pdf","grobid_xml":"https://content.openalex.org/works/W7223671710.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W2295598076","https://openalex.org/W2804479173","https://openalex.org/W2955655271","https://openalex.org/W2963275094","https://openalex.org/W3123998511","https://openalex.org/W3155259453","https://openalex.org/W3163165446","https://openalex.org/W3201527919","https://openalex.org/W3201990632","https://openalex.org/W3208090188","https://openalex.org/W3216516072","https://openalex.org/W4200328777","https://openalex.org/W4205982864","https://openalex.org/W4211011965","https://openalex.org/W4223978916","https://openalex.org/W4235256446","https://openalex.org/W4281673018","https://openalex.org/W4323838880","https://openalex.org/W4361001938","https://openalex.org/W4365813386","https://openalex.org/W4375867036","https://openalex.org/W4384483845","https://openalex.org/W4385586838","https://openalex.org/W4386813224","https://openalex.org/W4387190496","https://openalex.org/W4387221700","https://openalex.org/W4390245194","https://openalex.org/W4390874421","https://openalex.org/W4391352112","https://openalex.org/W4391441614","https://openalex.org/W4391605557","https://openalex.org/W4392198954","https://openalex.org/W4395114742","https://openalex.org/W4399029837","https://openalex.org/W4399053643","https://openalex.org/W4399387665","https://openalex.org/W4399736467","https://openalex.org/W4400284141","https://openalex.org/W4400306535","https://openalex.org/W4400605362","https://openalex.org/W4401253397","https://openalex.org/W4402704563","https://openalex.org/W4402978103","https://openalex.org/W4404190552","https://openalex.org/W4407840031","https://openalex.org/W4410083290","https://openalex.org/W4413063057","https://openalex.org/W4413096115","https://openalex.org/W4415344213","https://openalex.org/W4416180748","https://openalex.org/W4416768841","https://openalex.org/W4416818243","https://openalex.org/W7084067977","https://openalex.org/W7115593360","https://openalex.org/W7117293930","https://openalex.org/W7119801984","https://openalex.org/W7124462370","https://openalex.org/W7127045031","https://openalex.org/W7130348045"],"related_works":["https://openalex.org/W7128308457","https://openalex.org/W7163667084","https://openalex.org/W4410845250","https://openalex.org/W7214112372","https://openalex.org/W4400984943","https://openalex.org/W2921932194","https://openalex.org/W4405905337","https://openalex.org/W4403023321","https://openalex.org/W2889833238","https://openalex.org/W4323033702"],"abstract_inverted_index":{"Student":[0],"performance":[1,84,182,197,206,271],"prediction":[2,72,85,196,272],"has":[3],"become":[4],"an":[5,261],"important":[6],"research":[7],"area":[8,158],"in":[9,53,198,221],"educational":[10,50,275],"data":[11,31,276],"mining":[12],"because":[13],"it":[14],"supports":[15],"early":[16],"academic":[17,36,46,56,138],"interventions":[18],"and":[19,29,58,106,112,129,145,157,202,263],"decision-making":[20],"processes.":[21],"However,":[22],"most":[23,199,246],"previous":[24],"studies":[25],"assume":[26],"that":[27,68,176,245,256],"training":[28,111],"testing":[30],"originate":[32],"from":[33,230],"the":[34,100,104,160,164,168,194,205,217,222,227,239,248],"same":[35],"environment,":[37],"which":[38],"limits":[39],"model":[40,110,220],"generalization":[41],"when":[42],"applied":[43],"to":[44,98,185,232],"different":[45],"programs.":[47],"In":[48],"practical":[49],"settings,":[51],"variations":[52],"student":[54,83,270],"characteristics,":[55],"behavior,":[57],"learning":[59,80,116,179],"distributions":[60],"across":[61],"programs":[62,108],"introduce":[63],"substantial":[64],"cross-domain":[65,79,178,195,269],"distribution":[66,88,186],"shifts":[67],"can":[69],"significantly":[70,203],"reduce":[71],"performance.":[73],"This":[74],"study":[75],"proposes":[76],"a":[77],"CORAL-based":[78,146,190,257],"framework":[81,91,191],"for":[82,267],"under":[86,141,159,273],"shifting":[87],"conditions.":[89],"The":[90,172,188,211,253],"applies":[92],"CORrelation":[93],"ALignment":[94],"(CORAL)":[95],"domain":[96,258],"adaptation":[97,147,259],"align":[99],"feature":[101],"covariance":[102],"between":[103],"source":[105],"target":[107,224],"before":[109],"evaluation.":[113],"Four":[114],"machine":[115,127],"models,":[117],"namely":[118],"logistic":[119],"regression":[120],"(LR),":[121],"random":[122,218],"forest":[123,219],"(RF),":[124],"support":[125],"vector":[126],"(SVM),":[128],"extreme":[130],"gradient":[131],"boosting":[132],"(XGB),":[133],"were":[134,250],"evaluated":[135],"using":[136,152,238],"three":[137],"program":[139],"datasets":[140],"within-domain,":[142],"conventional":[143],"cross-domain,":[144],"settings.":[148],"Performance":[149],"was":[150,214],"assessed":[151],"accuracy,":[153],"precision,":[154],"recall,":[155],"F1-score,":[156],"curve":[161],"(AUC),":[162],"with":[163],"F1-score":[165,228],"used":[166],"as":[167],"primary":[169],"evaluation":[170],"metric.":[171],"experimental":[173,200],"results":[174],"demonstrate":[175],"baseline":[177],"consistently":[180],"experiences":[181],"degradation":[183],"owing":[184],"shifts.":[187],"proposed":[189],"successfully":[192],"improved":[193],"scenarios":[201],"reduced":[204],"gap":[207],"toward":[208],"within-domain":[209],"learning.":[210],"highest":[212],"improvement":[213],"achieved":[215],"by":[216],"ASY":[223],"domain,":[225],"where":[226],"increased":[229],"0.737":[231],"0.802":[233],"after":[234],"adaptation.":[235],"Statistical":[236],"analysis":[237],"Wilcoxon":[240],"signed-rank":[241],"test":[242],"further":[243],"confirmed":[244],"of":[247],"improvements":[249],"statistically":[251],"significant.":[252],"findings":[254],"indicate":[255],"provides":[260],"effective":[262],"computationally":[264],"efficient":[265],"solution":[266],"improving":[268],"heterogeneous":[274],"distribution.":[277]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-10-27T00:00:00"}
