PISA 2022. Predictors of the mathematics achievement of Spanish students in Secondary Education

被引:1
作者
Ortega-Rodriguez, Pablo Javier [1 ]
机构
[1] Univ Autonoma Madrid, Fac Formac Profesorado & Educ, Dept Pedag, Ave Francisco Tomas & Valiente 3 Campus Cantoblanc, Madrid 28049, Spain
来源
REVISTA DE PSICODIDACTICA | 2025年 / 30卷 / 01期
关键词
Math anxiety; Self-efficacy; Mathematics achievement; Multilevel modelling; PISA; MATH-ANXIETY; SOCIOECONOMIC-STATUS; SELF-EFFICACY; PERFORMANCE; STEM;
D O I
10.1016/j.psicod.2024.500152
中图分类号
G40 [教育学];
学科分类号
040101 ; 120403 ;
摘要
Mathematical competence is fundamental for active, participatory and engaged participation in the 21st century, and for facing the challenges of a globalised society. This study aims to analyse the simultaneous effect of a set of predictors on the mathematical competence of Spanish students who have participated in PISA 2022. The sample consists of 28,792 Spanish students (14,465 boys, 50.24%; 14,327 girls, 49.76%), from 935 schools. Two questionnaires are used to collect data: one for students and one for school principals. A hierarchical linear model is used according to the three levels presented by the data (Level 1 = Students, Level 2 = School and Level 3 = Autonomous Community). At the first level, sex, socioeconomic background, family cultural level, mathematics anxiety and self-efficacy is included; at the second level, the socio-economic background of the school; and at the third level, GDP per capita. The results show that girls experience more anxiety and less self-efficacy in mathematics than boys. The socio-economic background of the students, the cultural level of the family, mathematics anxiety and mathematics self-efficacy are significant predictors of mathematical competence. At school level, socioeconomic background has an impact on achievement. These findings suggest the need to strengthen collaboration between school and family, as well as to provide specific training for teachers on how to address socio-affective feelings towards mathematics. (c) 2024 Universidad de Pa & imath;<acute accent>s Vasco. Published by Elsevier Espana, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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页数:10
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