{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T15:06:31Z","timestamp":1780067191370,"version":"3.54.0"},"reference-count":34,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["RBIE"],"abstract":"<jats:p>Este estudo investiga a aplica\u00e7\u00e3o de modelos preditivos para estimar o \u00cdndice de Desenvolvimento da Educa\u00e7\u00e3o B\u00e1sica (IDEB) nos anos iniciais do ensino fundamental nos munic\u00edpios do estado do Piau\u00ed, a partir de vari\u00e1veis educacionais, socioecon\u00f4micas e financeiras. A pesquisa utilizou dados p\u00fablicos e, ap\u00f3s rigoroso processo de limpeza, agrega\u00e7\u00e3o, imputa\u00e7\u00e3o e sele\u00e7\u00e3o de atributos, constituiu uma base multivariada final composta por 1.262 registros e 126 vari\u00e1veis explicativas, extra\u00eddas exclusivamente de fontes oficiais. Foram avaliados dez algoritmos de Machine Learning (ML), incluindo regress\u00f5es lineares e penalizadas, \u00e1rvores de decis\u00e3o, m\u00e9todos baseados em vizinhan\u00e7a, ensembles e support vector regression e sete arquiteturas de Deep Learning (DL), abrangendo redes do tipo Multilayer Perceptron (MLP), variantes com Dropout e Batch Normalization, arquiteturas convolucionais (CNN), recorrentes (LSTM) e h\u00edbridas. Os experimentos seguiram uma abordagem quantitativa, com divis\u00e3o dos dados em conjuntos de treino e teste (70\/30), valida\u00e7\u00e3o cruzada k-fold, normaliza\u00e7\u00e3o quando necess\u00e1rio e an\u00e1lise de import\u00e2ncia de vari\u00e1veis. O algoritmo Extreme Gradient Boosting (XGBoost) apresentou o melhor desempenho m\u00e9dio entre os modelos avaliados, alcan\u00e7ando \ud835\udc45\u00b2 = 0,5542 no conjunto de teste e \ud835\udc45\u00b2 = 0,5455 \u00b1 0,0293 em valida\u00e7\u00e3o cruzada, com menores erros m\u00e9dios e maior estabilidade relativa em compara\u00e7\u00e3o \u00e0s abordagens lineares e \u00e0s redes neurais profundas. Al\u00e9m disso, a an\u00e1lise de import\u00e2ncia dos preditores identificou o PIB per capita municipal, a taxa de distor\u00e7\u00e3o idade-s\u00e9rie e a propor\u00e7\u00e3o de docentes sem forma\u00e7\u00e3o superior como os fatores mais relevantes para explicar a varia\u00e7\u00e3o do IDEB nos munic\u00edpios piauienses. Os achados contribuem para o aprimoramento de diagn\u00f3sticos regionais e para a proposi\u00e7\u00e3o de uma abordagem replic\u00e1vel de apoio ao monitoramento educacional em escala municipal.<\/jats:p>","DOI":"10.5753\/rbie.2026.7071","type":"journal-article","created":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T14:47:15Z","timestamp":1780066035000},"page":"717-738","source":"Crossref","is-referenced-by-count":0,"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"],"prefix":"10.5753","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8230-0274","authenticated-orcid":false,"given":"Maria Eva Clemencia Fonseca de Castro","family":"Silva","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5705-6932","authenticated-orcid":false,"given":"Ivan Saraiva","family":"Silva","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3391-8443","authenticated-orcid":false,"given":"Vin\u00edcius Ponte","family":"Machado","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"3742","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"1","unstructured":"Benevento, M. 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