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A data mining approach for predicting academic success – a case study
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Data mining Educational data mining Academic success Random forest Regression
Contexto Educativo
Citação
Martins, Maria Prudência; Miguéis, Vera; Fonseca, Davide; Alves, Albano (2019). A data mining approach for predicting academic success – a case study. In Information Technology and Systems: proceedings of ICITS 2019. 918, p. 45-56
Editora
Springer Nature Switzerland AG 2019