Examining students' achievement in mathematics: A multilevel analysis of the Programme for International Student Assessment (PISA) 2012 data for Greece. (2016)

• Record Type: Journal Article
• Authors: Karakolidis, Anastasios; Pitsia, Vasiliki; Emvalotis, Anastassios
• Is Part Of: International journal of educational research. Volume 79(2016)
• Page Start: 106
• Page End: 115
• Publication Date: 2016
• Subjects: Mathematics -- Multilevel modelling -- PISA; Education -- Evaluation -- Periodicals; Curriculum evaluation -- Periodicals; Education -- Periodicals; Program Evaluation -- Periodicals; Éducation -- Évaluation -- Périodiques; Programmes d'études -- Evaluation -- Périodiques; Éducation -- Recherche -- Périodiques; Curriculum evaluation; Education -- Evaluation; Periodicals; 370.72
• DOI: 10.1016/j.ijer.2016.05.013
• Languages: English
• ISSNs: 0883-0355

Abstract

Highlights: The school that students attend remarkably accounts for their mathematics performance. Background variables are statistically significant predictors of maths achievement. Students' self-beliefs about mathematics can predict their achievement. Individual and school ESCS statistically significantly predict mathematics achievement. The full model explain a large proportion of the variance in mathematics achievement. Abstract: The main aim of the present study was to carry out an in-depth examination of 15-year-olds' mathematics performance in Greece. By applying a multilevel model to the Programme for International Student Assessment 2012 data for Greece, this study investigated the factors, both at individual and school level, which were linked to mathematics achievement. The results revealed that gender, pre-primary education, self-beliefs about mathematics and individual and school mean socioeconomic status can statistically significantly predict students' mathematics achievement. The analysis also indicated the importance of the school which students attend in shaping their mathematics performance. Generally, it could be concluded that background characteristics, self-constructs and school level variables can explain a large proportion of the variance in students' mathematic achievement.