The reinforcement of global competence is vital for students to thrive in a rapidly changing world. This study explores the synergistic effects of both student and school factors on the classification of secondary students with high and low levels of global competence. Data are selected based on 208,556 secondary students from 6902 schools in 25 countries/regions and extracted from the Programme for International Student Assessment (PISA) 2018 datasets. Different from previous research, in this study, data science techniques, i.e., decision trees (DTs) and random forests (RFs), are adopted. Classification models are built to discriminate high achievers from low achievers and to discover the optimal set of factors with the most powerful impact on the discrimination of these two groups of achievers. The results show that both models have satisfactory classification abilities. According to the factor importance rankings in terms of discriminating global competence disparities, student factors play a major role. They especially emphasize students' capacities to examine global issues, students' awareness of intercultural communication, and teachers' attitudes toward different cultural groups.