Education is a basic human right; yet, not all children have equal access to educational opportunities. As inequality increases globally (OECD, 2018), students from socioeconomically disadvantaged families are at exceptional risk of underachieving and dropping out of school. This has long-term consequences for individuals and society at large (OECD, 2018). Despite having limited educational resources and disadvantaged home backgrounds, some students manage to succeed in school against the odds. These students demonstrate academic resilience, that is, the capacity to adapt in the face of adversity and achieve successful school performance (Martin & Marsh, 2006; Wang et al., 1994). However, it is yet unclear to what extent academic resilience may manifest differently across subjects and grades, especially by taking into account the different contexts of education systems around the world. Policymakers and other stakeholders have an urgent need to understand which malleable factors may support academic resilience in their education systems and what policies are appropriate for addressing these factors in their unique national contexts. Increased knowledge in these areas is key in preparing students to thrive in a rapidly changing world.

To address this knowledge gap in education worldwide, this current study draws upon unique data from the Trends in International Mathematics and Science Study (TIMSS), an international large-scale assessment with representative student samples that compares student performance in mathematics and science across participating countries. This study utilises multilevel data from students and school leaders across 33 education systems, including in America, Europe, Africa, Asia, and the Middle East to address the following research questions (RQs):

1. How prevalent are academically resilient students across the 33 education systems?
2. To what extent the share of academically resilient students varies across subjects (mathematics vs. science) and grades (Grade 4 vs. Grade 8) across these systems?
3. To what extent teaching and learning resources are associated with the share of academically resilient students within each system?

In general, resilience refers to the capacity to bounce back or recover from adverse circumstances (Masten et al., 1990). In education, academic resilience is concerned primarily with the increased likelihood of success in school despite experiencing adversity (Wang et al., 1994). Scholars have argued that resilience is a process that changes over time rather than a static personal trait (e.g., Egeland et al., 1993; Luthar et al., 2000). As a dynamic process, resilience varies within individuals and fluctuates over time and across contexts (Rutter, 2012). Resilience is also considered a multisystemic interaction between individuals and their environments (Masten, 2015; Ungar, 2021). These processes facilitate students’ adaptation to achieve better-than-expected outcomes in the face of adversity (Luthar et al., 2000; Rutter, 2012). By conceptualising resilience as a dynamic and multisystemic process, the construct can also be considered a result of the interplay between individual students and the contextual resources around them (Lerner, 2006; Wosnitza & Peixoto, 2018). This study focuses on the interplay between academic resilience and students’ access to resources for teaching and learning mathematics and science at schools. Indeed, knowledge about what types of school resources enable students to overcome their disadvantaged backgrounds and succeed in school is urgently needed to mitigate educational inequality. Such knowledge is valuable for assisting policymakers, practitioners, and researchers in providing all children with the opportunity to reach their full potential, regardless of their background. 

Data and measure This study utilises international large-scale assessment data from TIMSS 2019. Specifically, we analysed data from student assessment in mathematics and science in Grades 4 and 8 as well as student and principal questionnaires across 33 education systems. Academic resilience. As a context-specific, dynamic, and multisystemic phenomenon, resilience involves two core concepts: adversity and positive adaptation (Luthar et al., 2000; Ungar, 2021). As an indicator of adversity, we use student SES (socioeconomic status) using students’ responses to six questions related to their home educational resources, such as the number of books at home and possession of study supports. We applied the alignment method (Muthén & Asparouhov, 2018) to ensure the comparability of the SES construct across the 33 education systems. Student achievement is used as an indicator of positive adaption. Mathematics and science achievements were drawn from the TIMSS standardised test covering a wide range of domain-specific knowledge and difficulty levels. This study applies a within-country perspective to define academic resilience: students are academically resilient if they are among the bottom one-third of the SES distribution but achieve the top one-third of the performance distribution in their countries. To compare academic resilience across countries, we keep the bottom one-third of the SES distribution within countries but set the achievement score to 500 (the TIMSS achievement average). Teaching and learning resources. This measure is based on the principal’s report on general school resources and resources specific to mathematics and science instruction. It consists of (1) instruction affected by mathematics resource shortages scale, (2) instruction affected by science resource shortages scale, (3) school resources for science experiments. Data analysis For RQ1, logistic regression was fitted to estimate the likelihood of being resilient predicted by SES and achievement. We repeated the analysis to find the estimates across subjects (mathematics vs. science) and grades (Grade 4 vs. Grade 8). For RQ2, we tested the differences in the share of resilient students across subjects and grades using z-test for proportions. For RQ3, a two-level fixed slope logistic regression was applied at the student and school levels to examine the effects of teaching and learning resources on the likelihood of being high achievers for disadvantaged students. Data are handled and prepared in SPSS, and analyses are conducted in Mplus (Version 8.4; Muthén & Muthén, 1998-2018). Missing data are handled by the default Full Information Maximum Likelihood (FIML) method in Mplus.

For RQ1, the share of resilient students varies across education systems. Across subjects and grades, more than 70% of disadvantaged students are resilient in the East Asian countries and regions, such as Japan, South Korea, and Singapore. In contrast, less than 25% of disadvantaged students are considered resilient in other countries and regions like South Africa, Morocco, Saudi Arabia, and Iran. This study also shows how the distribution of academically resilient students varied when different thresholds were used (within and across countries perspectives). For RQ2, the overall share of resilient students was higher in Grade 4 than Grade 8. While there were no significant differences in the overall share of resilient students between mathematics and science in Grade 4, the share was significantly higher for science compared to mathematics in Grade 8. We also found within-country differences in the share of resilient students across subjects and grades. For RQ3, the effects of teaching and learning resources on the likelihood that a student from low SES families become resilient varied across countries, subjects, and grades. For instance, In France, all resources were positively correlated with the share of resilient students in both grades, whereas in England, the same resources were correlated with the share of resilient students in Grade 4 but not in Grade 8. This study also supports previous findings on the importance of school resources in improving the achievement of disadvantaged students (e.g., Gigliotti & Sorensen, 2018; Yang & Lee, 2022). Since TIMSS includes a nationally representative sample of students, findings from this study may provide the potential for generalizability to inform educational policy and practice at the national level. Given the right policies, all countries can address educational inequality and contributes to creating a future that leaves no one behind. Poverty does not need to be destiny.

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