{"id":31803,"date":"2025-08-11T16:43:11","date_gmt":"2025-08-11T21:43:11","guid":{"rendered":"https:\/\/blogs.funiber.org\/pt\/?p=31803"},"modified":"2025-08-28T16:48:55","modified_gmt":"2025-08-28T21:48:55","slug":"machine-learning-rendimiento-academico","status":"publish","type":"post","link":"https:\/\/www.funiber.blog\/pt\/formacao-professores\/2025\/08\/11\/machine-learning-rendimiento-academico","title":{"rendered":"Machine Learning na educa\u00e7\u00e3o: previs\u00e3o do desempenho acad\u00eamico"},"content":{"rendered":"\n

O desempenho acad\u00eamico universit\u00e1rio \u00e9 um indicador-chave do sucesso educacional, mas tamb\u00e9m um desafio constante para institui\u00e7\u00f5es e estudantes. Um estudo recente publicado na Revista Comunicar<\/strong> aborda como o uso do Machine Learning pode transformar a maneira como os fatores que influenciam o desempenho dos alunos s\u00e3o identificados e abordados.<\/p>\n\n\n\n

A pesquisa e sua abordagem<\/strong><\/h2>\n\n\n\n

O estudo, realizado no Equador, analisou dados de 12.211 estudantes universit\u00e1rios para prever e explicar o desempenho acad\u00eamico. Utilizando uma abordagem quantitativa e n\u00e3o experimental, os pesquisadores aplicaram algoritmos de classifica\u00e7\u00e3o supervisionada, como Random Forest, XGBoost e CatBoost. Este \u00faltimo se mostrou o mais eficaz, alcan\u00e7ando uma recupera\u00e7\u00e3o de 70% para identificar alunos em risco de reprova\u00e7\u00e3o.<\/p>\n\n\n\n

Al\u00e9m disso, o m\u00e9todo SHAP (Shapley Additive Explanations) foi usado para interpretar os resultados e compreender como as vari\u00e1veis influenciam o desempenho. Entre os fatores mais determinantes, encontram-se aspectos acad\u00eamicos-institucionais, como faculdade, curso e n\u00edvel de estudos, seguidos por vari\u00e1veis emocionais e socioecon\u00f4micas.<\/p>\n\n\n\n

Principais conclus\u00f5es<\/strong><\/h2>\n\n\n\n

1. Fatores influentes<\/strong>:<\/p>\n\n\n\n

O desempenho acad\u00eamico \u00e9 condicionado principalmente por vari\u00e1veis institucionais (como aten\u00e7\u00e3o docente e desempenho anterior), bem como por fatores emocionais (estado emocional) e sociodemogr\u00e1ficos.<\/p>\n\n\n\n

2. Impacto do Machine Learning<\/strong>:<\/p>\n\n\n\n

A capacidade preditiva dos modelos utilizados demonstra o potencial dessas ferramentas para identificar alunos em risco e projetar interven\u00e7\u00f5es personalizadas.<\/p>\n\n\n\n

3. Interpreta\u00e7\u00e3o dos dados<\/strong>:<\/p>\n\n\n\n

O uso do SHAP permitiu aos pesquisadores n\u00e3o apenas prever resultados, mas tamb\u00e9m entender as raz\u00f5es por tr\u00e1s deles, facilitando a tomada de decis\u00f5es informadas no \u00e2mbito educacional.<\/p>\n\n\n

\n
\"\"
O Machine Learning n\u00e3o apenas prev\u00ea resultados, mas explica as raz\u00f5es por tr\u00e1s deles, transformando a educa\u00e7\u00e3o.<\/em><\/figcaption><\/figure><\/div>\n\n\n

Implica\u00e7\u00f5es para a educa\u00e7\u00e3o<\/strong><\/h2>\n\n\n\n

O estudo destaca a import\u00e2ncia de integrar tecnologias avan\u00e7adas, como o aprendizado de m\u00e1quina, na gest\u00e3o educacional. Essas ferramentas n\u00e3o apenas melhoram a precis\u00e3o na identifica\u00e7\u00e3o de problemas, mas tamb\u00e9m oferecem uma base s\u00f3lida para implementar estrat\u00e9gias de apoio mais eficazes.<\/p>\n\n\n\n

Por outro lado, as descobertas ressaltam a necessidade de considerar fatores emocionais e sociais na elabora\u00e7\u00e3o de pol\u00edticas educacionais. A aten\u00e7\u00e3o integral ao aluno, que combine apoio acad\u00eamico e emocional, \u00e9 fundamental para melhorar os \u00edndices de reten\u00e7\u00e3o e sucesso universit\u00e1rio.<\/p>\n\n\n\n

Continue sua carreira profissional<\/strong><\/h2>\n\n\n\n

Se voc\u00ea tem interesse em explorar como a tecnologia pode transformar a educa\u00e7\u00e3o, o Mestrado em Educa\u00e7\u00e3o<\/u><\/a> da FUNIBER oferece as ferramentas necess\u00e1rias para liderar projetos inovadores neste campo. Aprenda a aplicar solu\u00e7\u00f5es tecnol\u00f3gicas para enfrentar os desafios educacionais do s\u00e9culo XXI. Inscreva-se agora e fa\u00e7a a diferen\u00e7a!<\/p>\n\n\n\n

Fonte:
Revista Comunicar<\/u><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

O desempenho acad\u00eamico universit\u00e1rio \u00e9 um indicador-chave do sucesso educacional, mas tamb\u00e9m um desafio constante para institui\u00e7\u00f5es e estudantes. Um estudo recente publicado na Revista Comunicar aborda como o uso do Machine Learning pode transformar a maneira como os fatores que influenciam o desempenho dos alunos s\u00e3o identificados e abordados. A pesquisa e sua abordagem […]<\/p>\n","protected":false},"author":33,"featured_media":31804,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_newsletter_tier_id":0,"footnotes":"","jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","enabled":false}}},"categories":[5],"tags":[10555,10557,9069,7792,321,10554,609,10012,10553,10552,10556,9826],"jetpack_publicize_connections":[],"yoast_head":"\nMachine Learning na educa\u00e7\u00e3o: previs\u00e3o do desempenho acad\u00eamico - FuniBlogs - FUNIBER<\/title>\n<meta name=\"description\" content=\"Descubra como o Machine Learning est\u00e1 revolucionando a previs\u00e3o do desempenho acad\u00eamico universit\u00e1rio e seu impacto na educa\u00e7\u00e3o\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/blogs.funiber.org\/pt\/formacao-professores\/2025\/08\/11\/machine-learning-rendimiento-academico\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning na educa\u00e7\u00e3o: previs\u00e3o do desempenho acad\u00eamico - 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