{"id":"https://openalex.org/W7162806706","doi":"https://doi.org/10.5753/rbie.2026.7071","title":"Aplica\u00e7\u00e3o de T\u00e9cnicas de Machine Learning e Deep Learning para Prever o IDEB Municipal Piauiense nos Anos Iniciais do Ensino Fundamental","display_name":"Aplica\u00e7\u00e3o de T\u00e9cnicas de Machine Learning e Deep Learning para Prever o IDEB Municipal Piauiense nos Anos Iniciais do Ensino Fundamental","publication_year":2026,"publication_date":"2026-05-19","ids":{"openalex":"https://openalex.org/W7162806706","doi":"https://doi.org/10.5753/rbie.2026.7071"},"language":null,"primary_location":{"id":"doi:10.5753/rbie.2026.7071","is_oa":true,"landing_page_url":"https://doi.org/10.5753/rbie.2026.7071","pdf_url":"https://journals-sol.sbc.org.br/index.php/rbie/article/download/7071/4017","source":{"id":"https://openalex.org/S2764357793","display_name":"Revista Brasileira de Inform\u00e1tica na Educa\u00e7\u00e3o","issn_l":"1414-5685","issn":["1414-5685","2317-6121"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"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":"Revista Brasileira de Inform\u00e1tica na Educa\u00e7\u00e3o","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://journals-sol.sbc.org.br/index.php/rbie/article/download/7071/4017","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103539979","display_name":"M.E.C. Silva","orcid":null},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Maria Eva Clemencia Fonseca de Castro Silva","raw_affiliation_strings":["Universidade Federal do Piau\u00ed"],"raw_orcid":"https://orcid.org/0009-0000-8230-0274","affiliations":[{"raw_affiliation_string":"Universidade Federal do Piau\u00ed","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065507627","display_name":"Ivan Saraiva Silva","orcid":"https://orcid.org/0000-0002-5705-6932"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Ivan Saraiva Silva","raw_affiliation_strings":["Universidade Federal do Piau\u00ed"],"raw_orcid":"https://orcid.org/0000-0002-5705-6932","affiliations":[{"raw_affiliation_string":"Universidade Federal do Piau\u00ed","institution_ids":["https://openalex.org/I3121799822"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052544532","display_name":"Vin\u00edcius Ponte Machado","orcid":"https://orcid.org/0000-0003-3391-8443"},"institutions":[{"id":"https://openalex.org/I3121799822","display_name":"Universidade Federal do Piau\u00ed","ror":"https://ror.org/00kwnx126","country_code":"BR","type":"education","lineage":["https://openalex.org/I3121799822"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Vin\u00edcius Ponte Machado","raw_affiliation_strings":["Universidade Federal do Piau\u00ed"],"raw_orcid":"https://orcid.org/0000-0003-3391-8443","affiliations":[{"raw_affiliation_string":"Universidade Federal do Piau\u00ed","institution_ids":["https://openalex.org/I3121799822"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I3121799822"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67509369,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"34","issue":null,"first_page":"717","last_page":"738"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12363","display_name":"Academic Research in Diverse Fields","score":0.05849999934434891,"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"}},"topics":[{"id":"https://openalex.org/T12363","display_name":"Academic Research in Diverse Fields","score":0.05849999934434891,"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/T14241","display_name":"Evasion and Academic Success Factors","score":0.05209999904036522,"subfield":{"id":"https://openalex.org/subfields/3317","display_name":"Demography"},"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/T13564","display_name":"Education and Public Policy","score":0.05000000074505806,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"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/linear-regression","display_name":"Linear regression","score":0.36239999532699585},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.359499990940094},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.35850000381469727},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.35179999470710754},{"id":"https://openalex.org/keywords/multilayer-perceptron","display_name":"Multilayer perceptron","score":0.313400000333786},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.29019999504089355}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4966000020503998},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4596000015735626},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4041000008583069},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.399399995803833},{"id":"https://openalex.org/C15708023","wikidata":"https://www.wikidata.org/wiki/Q80083","display_name":"Humanities","level":1,"score":0.387800008058548},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3644999861717224},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.36239999532699585},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.359499990940094},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.35850000381469727},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C179717631","wikidata":"https://www.wikidata.org/wiki/Q2991667","display_name":"Multilayer perceptron","level":3,"score":0.313400000333786},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.3109000027179718},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.289900004863739},{"id":"https://openalex.org/C152732102","wikidata":"https://www.wikidata.org/wiki/Q13434396","display_name":"Variance inflation factor","level":4,"score":0.2892000079154968},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.28299999237060547},{"id":"https://openalex.org/C2986587452","wikidata":"https://www.wikidata.org/wiki/Q938438","display_name":"Statistical analysis","level":2,"score":0.2718999981880188},{"id":"https://openalex.org/C149769383","wikidata":"https://www.wikidata.org/wiki/Q7520804","display_name":"Simple linear regression","level":3,"score":0.267300009727478},{"id":"https://openalex.org/C2777686260","wikidata":"https://www.wikidata.org/wiki/Q144037","display_name":"Calculus (dental)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.5753/rbie.2026.7071","is_oa":true,"landing_page_url":"https://doi.org/10.5753/rbie.2026.7071","pdf_url":"https://journals-sol.sbc.org.br/index.php/rbie/article/download/7071/4017","source":{"id":"https://openalex.org/S2764357793","display_name":"Revista Brasileira de Inform\u00e1tica na Educa\u00e7\u00e3o","issn_l":"1414-5685","issn":["1414-5685","2317-6121"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"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":"Revista Brasileira de Inform\u00e1tica na Educa\u00e7\u00e3o","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.5753/rbie.2026.7071","is_oa":true,"landing_page_url":"https://doi.org/10.5753/rbie.2026.7071","pdf_url":"https://journals-sol.sbc.org.br/index.php/rbie/article/download/7071/4017","source":{"id":"https://openalex.org/S2764357793","display_name":"Revista Brasileira de Inform\u00e1tica na Educa\u00e7\u00e3o","issn_l":"1414-5685","issn":["1414-5685","2317-6121"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"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":"Revista Brasileira de Inform\u00e1tica na Educa\u00e7\u00e3o","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7162806706.pdf","grobid_xml":"https://content.openalex.org/works/W7162806706.grobid-xml"},"referenced_works_count":12,"referenced_works":["https://openalex.org/W2121733691","https://openalex.org/W2135046866","https://openalex.org/W2156665896","https://openalex.org/W2295598076","https://openalex.org/W2981863007","https://openalex.org/W3139174706","https://openalex.org/W3166184106","https://openalex.org/W4283744009","https://openalex.org/W4321786774","https://openalex.org/W4401644271","https://openalex.org/W4402266222","https://openalex.org/W4413318601"],"related_works":[],"abstract_inverted_index":{"Este":[0],"estudo":[1],"investiga":[2],"a":[3,30,206,218,224,238,256],"aplica\u00e7\u00e3o":[4],"de":[5,12,32,47,53,70,77,87,101,137,150,152,175,208,220,226,251,258,262],"modelos":[6,167],"preditivos":[7],"para":[8,236,248,255],"estimar":[9],"o":[10,161,213,249],"\u00cdndice":[11],"Desenvolvimento":[13],"da":[14],"Educa\u00e7\u00e3o":[15],"B\u00e1sica":[16],"(IDEB)":[17],"nos":[18,24,242],"anos":[19],"iniciais":[20],"do":[21,26,28,107,240],"ensino":[22],"fundamental":[23],"munic\u00edpios":[25,243],"estado":[27],"Piau\u00ed,":[29],"partir":[31],"vari\u00e1veis":[33,66],"educacionais,":[34],"socioecon\u00f4micas":[35],"e":[36,51,64,84,94,98,115,123,139,148,177,190,199,223,254],"financeiras.":[37],"A":[38],"pesquisa":[39],"utilizou":[40],"dados":[41,134],"p\u00fablicos":[42],"e,":[43],"ap\u00f3s":[44],"rigoroso":[45],"processo":[46],"limpeza,":[48],"agrega\u00e7\u00e3o,":[49],"imputa\u00e7\u00e3o":[50],"sele\u00e7\u00e3o":[52],"atributos,":[54],"constituiu":[55],"uma":[56,128,259],"base":[57],"multivariada":[58],"final":[59],"composta":[60],"por":[61],"1.262":[62],"registros":[63],"126":[65],"explicativas,":[67],"extra\u00eddas":[68],"exclusivamente":[69],"fontes":[71],"oficiais.":[72],"Foram":[73],"avaliados":[74],"dez":[75],"algoritmos":[76],"Machine":[78],"Learning":[79,103],"(ML),":[80],"incluindo":[81],"regress\u00f5es":[82],"lineares":[83,198],"penalizadas,":[85],"\u00e1rvores":[86],"decis\u00e3o,":[88],"m\u00e9todos":[89],"baseados":[90],"em":[91,135,183,194,267],"vizinhan\u00e7a,":[92],"ensembles":[93],"support":[95],"vector":[96],"regression":[97],"sete":[99],"arquiteturas":[100,118],"Deep":[102],"(DL),":[104],"abrangendo":[105],"redes":[106,201],"tipo":[108],"Multilayer":[109],"Perceptron":[110],"(MLP),":[111],"variantes":[112],"com":[113,131,186],"Dropout":[114],"Batch":[116],"Normalization,":[117],"convolucionais":[119],"(CNN),":[120],"recorrentes":[121],"(LSTM)":[122],"h\u00edbridas.":[124],"Os":[125,245],"experimentos":[126],"seguiram":[127],"abordagem":[129,260],"quantitativa,":[130],"divis\u00e3o":[132],"dos":[133,210],"conjuntos":[136],"treino":[138],"teste":[140,176],"(70/30),":[141],"valida\u00e7\u00e3o":[142,184],"cruzada":[143],"k-fold,":[144],"normaliza\u00e7\u00e3o":[145],"quando":[146],"necess\u00e1rio":[147],"an\u00e1lise":[149,207],"import\u00e2ncia":[151,209],"vari\u00e1veis.":[153],"O":[154],"algoritmo":[155],"Extreme":[156],"Gradient":[157],"Boosting":[158],"(XGBoost)":[159],"apresentou":[160],"melhor":[162],"desempenho":[163],"m\u00e9dio":[164],"entre":[165],"os":[166,232],"avaliados,":[168],"alcan\u00e7ando":[169],"\ud835\udc45\u00b2":[170,178],"=":[171,179],"0,5542":[172],"no":[173],"conjunto":[174],"0,5455":[180],"\u00b1":[181],"0,0293":[182],"cruzada,":[185],"menores":[187],"erros":[188],"m\u00e9dios":[189],"maior":[191],"estabilidade":[192],"relativa":[193],"compara\u00e7\u00e3o":[195],"\u00e0s":[196,200],"abordagens":[197],"neurais":[202],"profundas.":[203],"Al\u00e9m":[204],"disso,":[205],"preditores":[211],"identificou":[212],"PIB":[214],"per":[215],"capita":[216],"municipal,":[217],"taxa":[219],"distor\u00e7\u00e3o":[221],"idade-s\u00e9rie":[222],"propor\u00e7\u00e3o":[225],"docentes":[227],"sem":[228],"forma\u00e7\u00e3o":[229],"superior":[230],"como":[231],"fatores":[233],"mais":[234],"relevantes":[235],"explicar":[237],"varia\u00e7\u00e3o":[239],"IDEB":[241],"piauienses.":[244],"achados":[246],"contribuem":[247],"aprimoramento":[250],"diagn\u00f3sticos":[252],"regionais":[253],"proposi\u00e7\u00e3o":[257],"replic\u00e1vel":[261],"apoio":[263],"ao":[264],"monitoramento":[265],"educacional":[266],"escala":[268],"municipal.":[269]},"counts_by_year":[],"updated_date":"2026-05-31T06:10:14.724740","created_date":"2026-05-30T00:00:00"}