{"id":"https://openalex.org/W7225327228","doi":"https://doi.org/10.1007/s44163-026-02724-9","title":"AI, governance and federated models for student dropout prediction in education","display_name":"AI, governance and federated models for student dropout prediction in education","publication_year":2026,"publication_date":"2026-11-05","ids":{"openalex":"https://openalex.org/W7225327228","doi":"https://doi.org/10.1007/s44163-026-02724-9"},"language":"en","primary_location":{"id":"doi:10.1007/s44163-026-02724-9","doi":"https://doi.org/10.1007/s44163-026-02724-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02724-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02724-9.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","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02724-9.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5147570489","display_name":"Srinivas Jadhav","orcid":"https://orcid.org/0009-0004-5328-6425","observed_orcids":["https://orcid.org/0009-0004-5328-6425"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Srinivas Jadhav","raw_affiliation_strings":["Justernet Corporation, 1755 Park Street, STE 200, Naperville, IL, 60563, USA"],"raw_orcid":"https://orcid.org/0009-0004-5328-6425","affiliations":[{"raw_affiliation_string":"Justernet Corporation, 1755 Park Street, STE 200, Naperville, IL, 60563, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5160807504","display_name":"Ashok Tadamalle","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I212738717","display_name":"Dwarkadas J. Sanghvi College of Engineering","ror":"https://ror.org/04d4hxn32","country_code":"IN","type":"education","lineage":["https://openalex.org/I212738717"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ashok Tadamalle","raw_affiliation_strings":["Dwarkadas J. Sanghvi College of Engineering, Vile Parle, Mumbai, Maharashtra, 400056, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dwarkadas J. Sanghvi College of Engineering, Vile Parle, Mumbai, Maharashtra, 400056, India","institution_ids":["https://openalex.org/I212738717"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5147570489"],"corresponding_institution_ids":[],"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":0.0,"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":0.9883000254631042,"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":0.9883000254631042,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.01119999960064888,"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/T14414","display_name":"Artificial Intelligence in Education","score":0.0,"subfield":{"id":"https://openalex.org/subfields/3304","display_name":"Education"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/student-dropout-prediction","display_name":"student dropout prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/federated-learning","display_name":"federated learning","score":0.9580000042915344},{"id":"https://openalex.org/keywords/data-privacy","display_name":"data privacy","score":0.7350000143051147},{"id":"https://openalex.org/keywords/data-governance","display_name":"data governance","score":0.8500000238418579},{"id":"https://openalex.org/keywords/student-data-protection","display_name":"student data protection","score":0.7099999785423279},{"id":"https://openalex.org/keywords/ferpa","display_name":"FERPA","score":0.8180000185966492},{"id":"https://openalex.org/keywords/gdpr","display_name":"GDPR","score":0.9269999861717224},{"id":"https://openalex.org/keywords/non-iid-data","display_name":"non-IID data","score":0.8149999976158142},{"id":"https://openalex.org/keywords/ensemble-learning","display_name":"ensemble learning","score":0.6159999966621399},{"id":"https://openalex.org/keywords/catboost","display_name":"CatBoost","score":0.8040000200271606},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"logistic regression","score":0.7360000014305115},{"id":"https://openalex.org/keywords/interpretable-models","display_name":"interpretable models","score":0.7559999823570251},{"id":"https://openalex.org/keywords/open-university-learning-analytics-dataset","display_name":"Open University Learning Analytics Dataset","score":0.8790000081062317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6541000008583069},{"id":"https://openalex.org/C2777648619","wikidata":"https://www.wikidata.org/wiki/Q2845208","display_name":"Learning analytics","level":2,"score":0.5680000185966492},{"id":"https://openalex.org/C39389867","wikidata":"https://www.wikidata.org/wiki/Q380767","display_name":"Corporate governance","level":2,"score":0.4618000090122223},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.43799999356269836},{"id":"https://openalex.org/C79158427","wikidata":"https://www.wikidata.org/wiki/Q485396","display_name":"Analytics","level":2,"score":0.4124000072479248},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40540000796318054},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4007999897003174},{"id":"https://openalex.org/C196879817","wikidata":"https://www.wikidata.org/wiki/Q872685","display_name":"Data governance","level":4,"score":0.37779998779296875},{"id":"https://openalex.org/C2777598771","wikidata":"https://www.wikidata.org/wiki/Q5341279","display_name":"Educational data mining","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C69360830","wikidata":"https://www.wikidata.org/wiki/Q1172237","display_name":"Data Protection Act 1998","level":2,"score":0.367000013589859},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.35659998655319214},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C71745522","wikidata":"https://www.wikidata.org/wiki/Q2476929","display_name":"Confidentiality","level":2,"score":0.33559998869895935},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.33000001311302185},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.32679998874664307},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3188000023365021},{"id":"https://openalex.org/C123201435","wikidata":"https://www.wikidata.org/wiki/Q456632","display_name":"Information privacy","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C175801342","wikidata":"https://www.wikidata.org/wiki/Q1988917","display_name":"Data analysis","level":2,"score":0.3089999854564667},{"id":"https://openalex.org/C120894424","wikidata":"https://www.wikidata.org/wiki/Q1322871","display_name":"Exploratory data analysis","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C184356942","wikidata":"https://www.wikidata.org/wiki/Q830382","display_name":"Best practice","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2838999927043915},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.2802000045776367},{"id":"https://openalex.org/C49895821","wikidata":"https://www.wikidata.org/wiki/Q5227368","display_name":"Data verification","level":2,"score":0.2786000072956085},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.27149999141693115},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.27079999446868896},{"id":"https://openalex.org/C53059260","wikidata":"https://www.wikidata.org/wiki/Q374758","display_name":"Multilevel model","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.25920000672340393}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s44163-026-02724-9","doi":"https://doi.org/10.1007/s44163-026-02724-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02724-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02724-9.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:de356edb570d46e08e2bd2d7ed9f3740","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/de356edb570d46e08e2bd2d7ed9f3740","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/s44163-026-02724-9","doi":"https://doi.org/10.1007/s44163-026-02724-9","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s44163-026-02724-9","pdf_url":"https://link.springer.com/content/pdf/10.1007/s44163-026-02724-9.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,"display_name":"Quality education","id":"https://metadata.un.org/sdg/4"},{"score":0.47620001435279846,"display_name":"Peace, justice, and strong institutions","id":"https://metadata.un.org/sdg/16"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225327228.pdf","grobid_xml":"https://content.openalex.org/works/W7225327228.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2769342982","https://openalex.org/W2978648093","https://openalex.org/W3023244064","https://openalex.org/W3197552266","https://openalex.org/W4312211780","https://openalex.org/W4321371811","https://openalex.org/W4406093375","https://openalex.org/W4409175404","https://openalex.org/W4410537793","https://openalex.org/W4411830792","https://openalex.org/W4416794533","https://openalex.org/W7167638311"],"related_works":["https://openalex.org/W4413034900","https://openalex.org/W7164186132","https://openalex.org/W7130593168","https://openalex.org/W7203875890","https://openalex.org/W4412966588","https://openalex.org/W7165570276","https://openalex.org/W4321371811","https://openalex.org/W7140180638","https://openalex.org/W4416681918","https://openalex.org/W7165132267"],"abstract_inverted_index":{"Educational":[0,54],"institutions":[1,28,106,270],"are":[2],"using":[3,130],"Artificial":[4],"Intelligence":[5],"and":[6,16,42,56,65,92,112,149,160,187,241,283],"learning":[7],"analytics":[8],"to":[9,20,48,77],"predict":[10],"student":[11,25,32,109,273],"performance,":[12,282],"identify":[13],"dropout":[14],"risks,":[15],"provide":[17],"early":[18,100],"support":[19],"students.":[21],"These":[22,80,264],"tools":[23],"improve":[24],"outcomes,":[26],"but":[27,254],"usually":[29],"keep":[30,272],"sensitive":[31],"data":[33,78,87,115,205,253],"in":[34,280],"a":[35,141,277,292],"centralized":[36,124,139,191,214,250],"location.":[37],"This":[38,94],"raises":[39],"serious":[40],"privacy":[41],"governance":[43,116],"concerns,":[44],"which":[45],"can":[46,271],"lead":[47],"possible":[49],"non-compliance":[50],"with":[51,114,126,168,249],"the":[52,121,131,169,173,176,229,267],"Family":[53],"Rights":[55],"Privacy":[57],"Act":[58],"(FERPA),":[59],"General":[60],"Data":[61,68],"Protection":[62,69],"Regulation":[63],"(GDPR),":[64],"Digital":[66],"Personal":[67],"(DPDP)":[70],"Act,":[71],"as":[72,74,172,288],"well":[73],"increased":[75],"exposure":[76],"breaches.":[79],"legal":[81],"frameworks":[82],"govern":[83],"how":[84],"students\u2019":[85],"personal":[86],"should":[88],"be":[89],"collected,":[90],"processed,":[91],"protected.":[93],"work":[95],"focuses":[96],"on":[97,201,207,260],"building":[98],"accurate":[99],"warning":[101],"AI":[102],"models":[103,125,179,198,246,286],"across":[104],"multiple":[105],"while":[107,224],"keeping":[108],"records":[110,274],"local":[111,252,275],"aligning":[113],"requirements.":[117],"The":[118,137],"methodology":[119],"compares":[120],"performance":[122],"of":[123,144,158,163],"governance-friendly,":[127],"federated":[128,225,258],"training":[129,215],"Open":[132],"University":[133],"Learning":[134],"Analytics":[135],"Dataset.":[136],"best":[138,248],"model,":[140],"soft-voting":[142],"ensemble":[143],"CatBoost,":[145],"CNN":[146],"+":[147,151],"MLP,":[148,152],"Transformer":[150],"achieves":[153],"an":[154,161],"at-risk":[155,170,185],"F1":[156],"score":[157],"0.80":[159],"AUC":[162,189],"0.89.":[164],"In":[165],"our":[166],"re-run":[167],"class":[171],"positive":[174],"class,":[175],"strongest":[177],"tree":[178],"reach":[180],"about":[181,236],"0.81":[182,184,242],"accuracy,":[183,238],"F1,":[186,240],"0.89":[188,222],"under":[190,257],"training.":[192],"A":[193],"notable":[194],"finding":[195],"is":[196,228],"that":[197],"trained":[199],"locally":[200],"each":[202],"institution\u2019s":[203],"own":[204],"perform":[206,247],"par":[208],"with,":[209],"or":[210,251],"slightly":[211],"better":[212],"than,":[213],"(for":[216],"example,":[217],"local-only":[218],"logistic":[219,233,289],"regression":[220,234,290],"reaches":[221,235],"AUC),":[223],"averaging":[226,259],"(FedAvg)":[227],"weakest":[230],"setting":[231],"(federated":[232],"0.75":[237],"0.77":[239],"AUC).":[243],"More":[244],"complex":[245],"lose":[255],"ground":[256],"this":[261],"non-IID":[262],"data.":[263],"results":[265],"make":[266],"trade-off":[268],"clearer:":[269],"without":[276],"meaningful":[278],"loss":[279],"predictive":[281],"simple,":[284],"interpretable":[285],"such":[287],"remain":[291],"strong":[293],"governance-aligned":[294],"choice.":[295]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-06T00:00:00"}