• Resumo

    Mineração de Dados Educacionais Visando a Identificação da Evasão no Ensino Superior

    Data de publicação: 04/09/2020

    ABSTRACT
    This paper tackles the problem of dropout of undergraduate students
    in a private university, by using Educational Data Mining
    (EDM) techniques. The EDM is an emerging area, concerned with
    developing methods for exploring the increasingly large-scale data
    that come from educational settings and using those methods to
    better understand students and the settings which they learn in. In
    this work, EDM is used to identify profiles of students who withdraw
    from their engineering courses. The considered dataset is
    composed of 53 attributes, involving financial and academic aspects
    of 2,925 engineering students. Preliminary results have identified
    some attributes that are related to the dropout in engineering courses,
    such as: the semester of the year (students are more prone to
    dropout in the first half of the year), attendance, grades (in this
    case median is more important than the mean value) and number
    of credits in the previous semester, and the current semester the
    student is enrolled (students bellow the 5th semester have a higher
    tendency to dropout).

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