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A data mining approach for predicting academic success – a case study

Publication . Martins, Maria Prudência; Miguéis, Vera; Fonseca, Davide; Alves, Albano

The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure.

2019Conference paper

Open access

Data mining educacional: uma revisão da literatura

Publication . Martins, Maria Prudência; Miguéis, Vera; Fonseca, Davide

Com o objetivo de divulgar o potencial e a aptidão do Data Mining Educacional, como um instrumento de análise e de investigação, no apoio à gestão de instituições dedicadas ao ensino, apresenta-se, no presente artigo, uma sucinta descrição de alguns dos estudos mais relevantes da área. A análise efetuada permite evidenciar as inovações que o EDM tem vindo a promover, bem como as tendências de investigação atuais e futuras.

2018Conference paper

Open access