{"id":"https://openalex.org/W7122516323","doi":"https://doi.org/10.1007/s41060-025-00965-y","title":"An empirical evaluation of clustering processes for early detection of university dropout","display_name":"An empirical evaluation of clustering processes for early detection of university dropout","publication_year":2026,"publication_date":"2026-01-12","ids":{"openalex":"https://openalex.org/W7122516323","doi":"https://doi.org/10.1007/s41060-025-00965-y"},"language":"en","primary_location":{"id":"doi:10.1007/s41060-025-00965-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-025-00965-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00965-y.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00965-y.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5067261925","display_name":"Fran Melchor","orcid":null},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Fran Melchor","raw_affiliation_strings":["Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain"],"affiliations":[{"raw_affiliation_string":"Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039436477","display_name":"Jos\u00e9 Miguel Cantos Conejero","orcid":null},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jos\u00e9 M. Conejero","raw_affiliation_strings":["Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain"],"affiliations":[{"raw_affiliation_string":"Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076727888","display_name":"A. Fern\u00e1ndez-Garc\u00eda","orcid":null},"institutions":[{"id":"https://openalex.org/I52354020","display_name":"University of Almer\u00eda","ror":"https://ror.org/003d3xx08","country_code":"ES","type":"education","lineage":["https://openalex.org/I52354020"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Jes\u00fas Fern\u00e1ndez-Garc\u00eda","raw_affiliation_strings":["Applied Computing Group, University of Almer\u00eda, Almer\u00eda, 04120, Spain"],"affiliations":[{"raw_affiliation_string":"Applied Computing Group, University of Almer\u00eda, Almer\u00eda, 04120, Spain","institution_ids":["https://openalex.org/I52354020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122765419","display_name":"Fernando S\u00e1nchez-Figueroa","orcid":null},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Fernando S\u00e1nchez-Figueroa","raw_affiliation_strings":["Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain"],"affiliations":[{"raw_affiliation_string":"Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122842501","display_name":"Roberto Rodr\u00edguez-Echeverr\u00eda","orcid":null},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Roberto Rodr\u00edguez-Echeverr\u00eda","raw_affiliation_strings":["Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain"],"affiliations":[{"raw_affiliation_string":"Quercus, INTIA, Universidad de Extremadura, C\u00e1ceres, 10003, Espa\u00f1a, Spain","institution_ids":["https://openalex.org/I80606768"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5067261925"],"corresponding_institution_ids":["https://openalex.org/I80606768"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09605847,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","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":0.8745999932289124,"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":0.8745999932289124,"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/T10267","display_name":"Higher Education Research Studies","score":0.01360000018030405,"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"}},{"id":"https://openalex.org/T10637","display_name":"Advanced Clustering Algorithms Research","score":0.00989999994635582,"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/cluster-analysis","display_name":"Cluster analysis","score":0.7634000182151794},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.638700008392334},{"id":"https://openalex.org/keywords/novelty","display_name":"Novelty","score":0.588699996471405},{"id":"https://openalex.org/keywords/dropout","display_name":"Dropout (neural networks)","score":0.555400013923645},{"id":"https://openalex.org/keywords/preprocessor","display_name":"Preprocessor","score":0.5407000184059143},{"id":"https://openalex.org/keywords/data-pre-processing","display_name":"Data pre-processing","score":0.4595000147819519},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/data-integration","display_name":"Data integration","score":0.36309999227523804}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.7634000182151794},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.738099992275238},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.649399995803833},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.638700008392334},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5906999707221985},{"id":"https://openalex.org/C2778738651","wikidata":"https://www.wikidata.org/wiki/Q16546687","display_name":"Novelty","level":2,"score":0.588699996471405},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.555400013923645},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.5407000184059143},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.47760000824928284},{"id":"https://openalex.org/C10551718","wikidata":"https://www.wikidata.org/wiki/Q5227332","display_name":"Data pre-processing","level":2,"score":0.4595000147819519},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C2776650193","wikidata":"https://www.wikidata.org/wiki/Q264661","display_name":"Obstacle","level":2,"score":0.36309999227523804},{"id":"https://openalex.org/C2778924833","wikidata":"https://www.wikidata.org/wiki/Q7064603","display_name":"Novelty detection","level":3,"score":0.3407999873161316},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C120936955","wikidata":"https://www.wikidata.org/wiki/Q2155640","display_name":"Empirical research","level":2,"score":0.29580000042915344},{"id":"https://openalex.org/C39235581","wikidata":"https://www.wikidata.org/wiki/Q5158434","display_name":"Conceptual clustering","level":5,"score":0.2678999900817871},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C184509293","wikidata":"https://www.wikidata.org/wiki/Q5136711","display_name":"Clustering high-dimensional data","level":3,"score":0.2630999982357025},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.2551000118255615}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1007/s41060-025-00965-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-025-00965-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00965-y.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1007/s41060-025-00965-y","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s41060-025-00965-y","pdf_url":"https://link.springer.com/content/pdf/10.1007/s41060-025-00965-y.pdf","source":{"id":"https://openalex.org/S4210195017","display_name":"International Journal of Data Science and Analytics","issn_l":"2364-415X","issn":["2364-415X","2364-4168"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319972","host_organization_name":"Springer International Publishing","host_organization_lineage":["https://openalex.org/P4310319972","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer International Publishing","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Journal of Data Science and Analytics","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W7122516323.pdf"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1548056638","https://openalex.org/W1646999038","https://openalex.org/W1908619978","https://openalex.org/W1974209792","https://openalex.org/W1974729915","https://openalex.org/W1982658582","https://openalex.org/W1986548831","https://openalex.org/W2026360870","https://openalex.org/W2042442732","https://openalex.org/W2073404525","https://openalex.org/W2076361679","https://openalex.org/W2089542572","https://openalex.org/W2099581008","https://openalex.org/W2108323654","https://openalex.org/W2122943553","https://openalex.org/W2295351501","https://openalex.org/W2345267773","https://openalex.org/W2563345517","https://openalex.org/W2594975452","https://openalex.org/W2768623141","https://openalex.org/W2769280541","https://openalex.org/W2790136402","https://openalex.org/W2886306415","https://openalex.org/W2895942663","https://openalex.org/W2899104718","https://openalex.org/W2939408882","https://openalex.org/W2963174546","https://openalex.org/W2990986232","https://openalex.org/W3003555941","https://openalex.org/W3093878986","https://openalex.org/W3110435694","https://openalex.org/W3134993972","https://openalex.org/W3159287098","https://openalex.org/W3203763991","https://openalex.org/W4210464152","https://openalex.org/W4234001159","https://openalex.org/W4243794628","https://openalex.org/W4251529734","https://openalex.org/W4280582117","https://openalex.org/W4285264052","https://openalex.org/W4296743374","https://openalex.org/W4300672471","https://openalex.org/W4311137287","https://openalex.org/W4410800698"],"related_works":[],"abstract_inverted_index":{"Abstract":[0],"The":[1,94,197,269],"elevated":[2],"rates":[3],"of":[4,13,60,96,118,122,155,174,219,230,287],"dropout":[5],"within":[6,137],"academic":[7,29,243,292],"institutions":[8,104],"have":[9,129],"prompted":[10],"the":[11,58,80,119,145,156,180,186,203,217,220,228,253,264,288],"use":[12],"Artificial":[14],"Intelligence":[15],"(AI)":[16],"to":[17,45,73,84,115,133,178,190,262,275],"tackle":[18],"this":[19,47],"issue.":[20],"These":[21,140],"efforts":[22],"often":[23],"rely":[24],"mainly":[25],"on":[26],"administrative":[27],"and":[28,49,166,232,285],"data,":[30,62],"lacking":[31],"personal":[32],"information":[33],"about":[34],"students.":[35],"In":[36],"a":[37,55,100,172,239,246],"previous":[38],"study,":[39],"we":[40],"explored":[41],"machine":[42],"learning":[43],"models":[44],"leverage":[46],"data":[48,68,98,148,213,225,244,282],"harness":[50],"their":[51,74,125,209],"knowledge-extraction":[52],"capabilities.":[53],"However,":[54],"critical":[56],"factor,":[57],"availability":[59],"labeled":[61,97],"was":[63],"not":[64,107,200],"addressed.":[65],"Obtaining":[66],"these":[67],"may":[69,143],"be":[70,273],"challenging":[71],"due":[72],"distribution":[75],"across":[76],"different":[77],"systems":[78],"or":[79,280],"considerable":[81],"time":[82],"required":[83],"collect":[85],"them,":[86],"especially":[87],"when":[88,160],"new":[89],"degrees":[90],"are":[91,113,283,291],"being":[92],"implemented.":[93],"lack":[95],"is":[99,236],"major":[101],"obstacle":[102],"for":[103,124,147,184,227,252],"that":[105,111],"do":[106],"possess":[108],"them":[109],"so":[110],"they":[112],"unable":[114],"take":[116],"advantage":[117],"full":[120],"potential":[121],"AI":[123],"purposes.":[126],"Clustering":[127],"algorithms":[128,142,177],"conventionally":[130],"been":[131],"employed":[132],"uncover":[134],"latent":[135],"patterns":[136],"unlabeled":[138,195],"data.":[139,196],"unsupervised":[141],"reduce":[144],"need":[146],"labeling;":[149],"nonetheless,":[150],"it":[151],"necessitates":[152],"rigorous":[153],"validation":[154,251],"resulting":[157],"clusters,":[158],"particularly":[159],"dealing":[161],"with":[162,211],"datasets":[163],"encompassing":[164],"numerical":[165,231],"categorical":[167,233],"attributes.":[168],"This":[169],"paper":[170],"introduces":[171],"comparison":[173,205],"various":[175],"clustering":[176],"discern":[179],"most":[181,286],"appropriate":[182],"technique":[183],"uncovering":[185],"underlying":[187],"factors":[188,265],"contributing":[189],"university":[191],"student":[192],"attrition,":[193],"employing":[194],"novelty":[198],"lies":[199],"only":[201],"in":[202,208,293],"algorithmic":[204],"but":[206],"also":[207,257],"integration":[210],"diverse":[212],"preprocessing":[214],"methodologies,":[215],"streamlining":[216],"selection":[218],"optimal":[221],"combination":[222],"including":[223],"advanced":[224],"transformations":[226],"harmonization":[229],"information.":[234],"It":[235],"illustrated":[237],"through":[238],"real-world":[240],"case":[241],"utilizing":[242],"from":[245],"Spanish":[247],"university,":[248],"providing":[249],"empirical":[250],"proposed":[254],"methodology.":[255],"We":[256],"conducted":[258],"an":[259],"exploratory":[260],"analysis":[261],"identify":[263],"behind":[266],"cluster":[267],"formation.":[268],"insights":[270],"gained":[271],"can":[272],"extrapolated":[274],"analogous":[276],"experiments":[277],"where":[278],"social":[279],"economic":[281],"scarce,":[284],"available":[289],"attributes":[290],"nature.":[294]},"counts_by_year":[],"updated_date":"2026-03-11T06:11:40.159057","created_date":"2026-01-13T00:00:00"}
