Academic achievement is of great interest to education researchers and practitioners. Several academic achievement determinants have been described in the literature, mostly identified by analyzing primary (sample) data with classic statistical methods. Despite their superiority, only recently have machine learning methods started to be applied systematically in this context. However, even when this is the case, the ability to draw conclusions is greatly hampered by the "black-box" effect these methods entail. We contribute to the literature by combining the efficiency of machine learning methods, trained with data from virtually every public upper-secondary student of a European country, with the ability to quantify exactly how much each driver impacts academic achievement on Mathematics and mother tongue, through the use of prototypes. Our results indicate that the most important general academic achievement inhibitor is the previous retainment. Legal guardian's education is a critical driver, especially in Mathematics; whereas gender is especially important for mother tongue, as female students perform better. Implications for research and practice are presented. Doi: 10.28991/ESJ-2022-SIED-010 Full Text: PDF
Desde os anos 50 do século passado que o desempenho académico tem sido foco de interesse por parte de investigadores e decisores políticos. No entanto, apenas recentemente os métodos de ciência de dados começaram a ser aplicados de forma mais sistemática a este tema. Este trabalho utiliza os dados dos exames nacionais de matemática e português da população portuguesa no ano letivo 2018/2019 para, através de redes neuronais, avaliar e comparar quais os fatores que afetam os resultados desses exames, e de que forma. Além disso, uma nova abordagem é apresentada para lidar com o dilema da "caixa negra" dos métodos de ciências de dados mais avançados. Esta abordagem passa pela criação de um conjunto de protótipos através de Redes Neuronais, fornecendo uma estimativa de quanto cada potencial impacta o desempenho acadêmico.
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