Understanding academic achievement (AA) is one of the most global challenges, as there is evidence that it is deeply intertwined with economic development, employment, and countries' wellbeing. However, the research conducted on this topic grounds in traditional (statistical) methods employed in survey (sample) data. This paper presents a novel approach, using state-of-the-art artificial intelligence (AI) techniques to predict the academic achievement of virtually every public high school student in Portugal, i.e., 110,627 students in the academic year of 2014/2015. Different AI and non-AI methods are developed and compared in terms of performance. Moreover, important insights to policymakers are addressed.

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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

Understanding the determinants of academic achievement (AA) is crucial for virtually every stakeholder interested in personal development and individual and societal wellbeing. Extensive research in several areas, such as education, economics, or psychology, has addressed this topic, identifying a vast number of determinants that impact high school students’ AA. In this work, we perform a meta-analysis, including a weight analysis of 49 quantitative studies that investigate this topic, exploring the best predictors of high school students’ academic success. We also explore moderation effects. Our results show that academic self-efficacy and socioeconomic status are the best predictors of AA, and they are statistically significant. Other statistically significant predictors, albeit less common in the analyses, are mastery avoidance, motivation, sleep habits, and work avoidance. Implications for theory and practice and directions for future research are discussed.