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Student well-being and mathematical literacy performance in PISA 2018: a machine-learning approach.
- Source :
-
Educational Psychology . Feb-Apr2024, Vol. 44 Issue 3, p340-357. 18p. - Publication Year :
- 2024
-
Abstract
- One of the goals of the educational system is to promote the well-being of students due to its associated on their academic performance. This research aims to shed light on the main role of well-being variables (introduced by PISA 2018 for the first time, as far as our knowledge) in the mathematical competence throughout of the PISA 2018 evaluation with a sample of 35,943 Spanish students. Students ranged in age from 15 to 16 years old (SD = 0.288). Supervised learning techniques such as decision tree methodology, random forest, and a linear hierarchical model have been used throughout this study. The criterion variable was competency performance in mathematics, while the independent variables consisted of a total of 83 items extracted from the student well-being questionnaire. These predictors are grouped into five domains: physical, psychological, material, cognitive and social. We have proved that well-being plays an important role in mathematical understanding in PISA 2018. Specifically, social well-being is the most important variable in our study. To conclude, we observe that social well-being, contextualised in terms of the relationships that the students maintain with their teachers, peers and families, plays a detrimental role in mathematics achievement. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 01443410
- Volume :
- 44
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Educational Psychology
- Publication Type :
- Academic Journal
- Accession number :
- 177963977
- Full Text :
- https://doi.org/10.1080/01443410.2024.2359104