Why are cognitive abilities of children so different across countries? The link between major socioeconomic factors and PISA test scores

TL;DR: Marks et al. as mentioned in this paper found that SES has only moderate effects on student achievement, and its effects are especially weak when considering prior achievement, an important and relevant predictor.

TL;DR: The authors reviewed the literature in English between 1967-2020 with the aim to identify underlying explanatory factors and their relative importance with regard to educational achievement among East Asian schoolchildren (Grade 5-10) in Japan, China, South Korea, Taiwan, and Singapore.

TL;DR: In this article , machine learning approaches were used to analyze PISA data from the student questionnaire to test models that best identify the poorest performing Filipino students and explore factors that could help identify the students who are vulnerable to very low achievement in science and that could indicate possible targets for reform in science education in the Philippines.

TL;DR: 2009 SIMCE outcomes were positively and significantly associated with 2013 SIMCE and with 2017 PSU and, with 2013 PSU outcomes in school-age children from 2010 5ESG and 1HSG, respectively, and may be useful for educational and health planning in Chile and countries in a comparable stage of development.

TL;DR: Efron and Tibshirani as discussed by the authors used bootstrap tests to assess mediation, finding that the sampling distribution of the mediated effect is skewed away from 0, and they argued that R. M. Kenny's (1986) recommendation of first testing the X --> Y association for statistical significance should not be a requirement when there is a priori belief that the effect size is small or suppression is a possibility.

TL;DR: In this paper, the authors describe differences between moderator and mediator effects, and provide non-technical descriptions of how to examine each type of effect, including study design, analysis, and interpretation of results.

TL;DR: This chapter limits the discussion to three important elements of multivariate data analysis, namely, principal component analysis, cluster analysis, and discriminant analysis, in the context of data partitioning and pattern recognition for multiple regression.

TL;DR: The importance of undernutrition as an underlying cause of child deaths associated with infectious diseases, the effects of multiple concurrent illnesses, and recognition that pneumonia and diarrhoea remain the diseases that are most often associated with child deaths as mentioned in this paper.