Low-achievement risk assessment with machine learning

In this work, we propose a method for assessing the risk of low-achievement in secondary school with data collected from the Italian ministry of education. Low-achievement is a phenomenon whereby a student, despite completing his or her education, does not reach the level of competence expected by the school system. We train three machine learning models on a large, real dataset through the INVALSI large-scale assessment tests and compare the results in terms of predictive and descriptive performance. We exploit data collected in end-of-primary school mathematics tests to predict the risk of low-achievement at the end of compulsory schooling (5 years later). The promising results of our approach suggest that it is possible to generalise the methodology for other school systems and for different teaching subjects.