Mathematics and Mother Tongue Academic Achievement: A Machine Learning Approach
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
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DOI: 10.28991/ESJ-2022-SIED-010
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