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All Journal Emerging Science Journal
Beatriz-Afonso, Ana
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Environmental Science

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Unraveling the Myths of Rural vs. Urban Academic Achievement Drivers Beatriz-Afonso, Ana; Cruz-Jesus, Frederico
Emerging Science Journal Vol 8, No 6 (2024): December
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2024-08-06-010

Abstract

The generalized migration of individuals from rural to urban areas is a global phenomenon that entails many divides, education being one of them. However, there is a lack of understanding regarding whether the factors driving higher academic achievement (AA) differ between urban and rural students. This study uses data from almost every student in Portugal who took the Portuguese and/or mathematics high school national exams. By applying OLS, the aim is to identify the AA drivers and compare these drivers between urban and rural areas. Among the key findings, variables related to academic background emerged as the strongest predictors of AA, regardless of the environment. Additionally, ICT access is insignificant in urban and rural areas, while socio-economic status does not significantly impact AA amongst rural students. These findings highlight the need for tailored interventions that address the unique challenges faced by students in different areas, with a particular focus on enhancing academic support structures to improve educational outcomes. To the best of our knowledge, this study is the first to utilize data encompassing virtually every student in an entire country to compare and understand the differences in the determinants of AA between urban and rural areas. Doi: 10.28991/ESJ-2024-08-06-010 Full Text: PDF
Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach Nunes, Catarina; Beatriz-Afonso, Ana; Cruz-Jesus, Frederico; Oliveira, Tiago; Castelli, Mauro
Emerging Science Journal Vol. 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-SIED-010

Abstract

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
Co-Authors Castelli, Mauro Cruz-Jesus, Frederico Nunes, Catarina Oliveira, Tiago