The present study explored the effects of the national and cultural contexts on students' mathematics achievement. The study also investigated the nature and magnitude of student-level (level 1), school-level (level 2), and country-level (level 3) factors that are associated with math achievement. The Program for International Student Assessment (PISA) 2018 datasets were used. The main predictors focusing on this study included university admission procedure and the country's culture of mindsets about intelligence at level 3, indicating extra-curricular activities at level 2, growth mindset, and resilience self-efficacy at level 1. Other than main predictors, various predictors including country's characteristics, school characteristics, school climate factors, students' demographic characteristics, and non-cognitive abilities were added in the analysis to examine the main predictors are statistically significant after controlling for other predictors. The findings of HLM analysis showed that mathematics achievement is associated with national and cultural contexts since the study found 31.30% of the total variation was accounted for level 3 in math achievement. Also, the significant findings of the study indicated that university admission procedure was significantly associated with country-mean math achievement while the country's culture of mindsets about intelligence was not at level 3. At level 2, providing extra-curricular activities in school was a significant predictor for math achievement. At level 1, a growth mindset and information and Communication Technology (ICT) usage were positively associated with math achievement. The other significant predictors for math achievement were found in the model. In addition, the study found that the compositional effect of ICT usage explained a significant amount of between schools and countries variance even after controlling for other predictors in the analysis. Moreover, the study found several counterintuitive association phenomena due to shift of meaning. These findings were explained in terms of practical and theoretical implications for policymakers, educators, and researchers to improve students' mathematics achievement. / Doctor of Philosophy / Policymakers and researchers have been concerned about the shortage of students pursuing STEM disciplines in the United States despite the increasing demand for STEM professionals. Since mathematical skills play an important role in a nation's economic development, improving mathematics performance is essential for developing professional STEM workers. Therefore, conducting a cross-national comparative study of mathematics achievement is needed to provide a useful empirical perspective and deeper understanding of mathematics performance. The present study examined the association of diverse predictors at the country-, school-, and student-level with math achievement using multilevel modeling which is also called hierarchical linear modeling (HLM). It was found that university admission procedure was significantly associated with country-mean math achievement at the country-level. Also, providing extra-curricular activities in school was a significant predictor for math achievement at the school-level and a growth mindset and information and Communication Technology (ICT) usage were positively associated with math achievement at the student-level. In addition, the study found the positive compositional effect of ICT usage at school- and country-level which indicates that developing the infrastructure of ICT in school and country should be needed to for high and sustainable students' math achievement. Moreover, the study found several counterintuitive association phenomena due to shift of meaning. These findings were explained in terms of practical and theoretical implications for policymakers, educators, and researchers to improve students' mathematics achievement.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/113244 |
| Date | 18 January 2023 |
| Creators | Lee, Youjin |
| Contributors | Educational Research and Evaluation, Miyazaki, Yasuo, Jones, Brett D., Skaggs, Gary E., Kniola, David J. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial 4.0 International, http://creativecommons.org/licenses/by-nc/4.0/ |
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