The effects of students’ backgrounds, attitudes, and ICT familiarity on mathematical literacy: latent profile analysis and lasso regression

Mathematical literacy is becoming increasingly important and needs to be developed. However, various factors can affect students with different mathematical literacy patterns. This study aims to investigate the effects of students’ backgrounds, attitudes toward mathematics, and information and communication technology (ICT) familiarity on the mathematical literacy of different patterns of students. The data of 15,478 secondary school students from Hong Kong, Macau, and Taiwan in China, who participated in the Programme for International Student Assessment (PISA) 2022 was analyzed. It was found that students’ mathematical literacy can be classified into four clusters: low, medium, moderately high, and high mathematical literacy. In addition, students’ personal backgrounds, attitudes toward mathematics learning, and ICT familiarity can jointly predict mathematical literacy in different clusters. Students’ economic, social, and cultural status (ESCS), self-efficacy toward formal and applied mathematics, and use of ICT outside the classroom for school activities are positive predictors common to all four clusters. Meanwhile, the other variables have mixed predictive effects on different clusters.