Predicting Italian students’ mathematics outcomes

The present paper aims to investigate the factors that influence the achievements of Italian students on the National Mathematics Assessment INVALSI. The study is a quantitative non-experimental research and utilizes the Decision Tree Method (DTM), a data mining and machine learning approach, to analyze the relationships and interactions among the variables and their influence on students’ mathematics performance. The sample for the study consists of 15,344 grade-10 students who took the INVALSI test in the school year 2021/22. Findings show that school typology had the highest relative importance, followed by students’ school grades in mathematics, socioeconomic status, geographic macroregion, gender, age, and, finally, origin. Based on these results, policymakers and educators should prioritize interventions that enhance educational environments and individual academic proficiency, particularly focusing on school type, mathematics grades, and students’ ESCS, to improve student achievements and promote deeper learning.