Psychosocial predictors of mathematics performance and future occupation of mathematically gifted adolescents across sex in PISA 2022

This study aims to investigate Malaysian students’ performance in mathematics literacy from gender and socioeconomic perspectives based on the Programme for International Student Assessment (PISA) 2009+ and 2012 datasets. The results revealed that girls significantly scored about eight points higher than boys in mathematics literacy in PISA 2012. Additionally, girls significantly outperformed boys in all three mathematical content categories and processes. However, proportion of boys at school level had no significant interaction effect with gender and ESCS at student level on mathematics performance in PISA 2009+ and PISA 2012. Boys and girls performed equally after controlling socioeconomic status at student and school level. The significant influence of economic, social and cultural index on students’ mathematics literacy performance indicated the presence of socioeconomic inequity in mathematics literacy performance. Schools with high average of socioeconomic status outperformed schools with low socioeconomic status in mathematics literacy in both PISA 2009+ and PISA 2012. The socioeconomically disadvantaged students from school with low ESCS mean outperformed the socioeconomically disadvantaged students from school with high ESCS mean in PISA 2009+ and PISA 2012. The multilevel analyses showed that between-school variance explained was about 54 and 61 % in PISA 2009+ and PISA 2012, respectively. Implications and suggestions for future studies are presented.

International large-scale assessment studies provide comparative data countries both evaluate their education systems' performance and give information about the factors related students' achievement. The Programme for International Student Assessment (PISA) is one of these international assessment studies conducted by the Organization for Economic Co-operation and Development (OECD) every three year since 2000. The PISA target population consists of 15-year-old students and aims assess students' ability use their knowledge and skills in order meet real-life challenges (OECD, 2013b). Mathematics literacy was the primary focus of the 2012 assessments. The PISA defines mathematical literacy as an individual's capacity identify and understand the role that mathematics play in the world, make well-founded judgments, and use and engage with mathematics in ways that meet the needs of that individual's life as a constructive, concerned, and reflective citizen (OECD, 2009, p. 14).Research on factors related students' mathematics achievement and socioeconomic status (SES) has received broad attention. Various studies have examined socioeconomic status, finding it be one of the strongest predictors of academic achievement (Caldas & Bankston, 1997; Papanastasiou, 2000; Sirin, 2005) in not only international large-scale assessments (Chiu, Chow, & Mcbridge-Chang, 2007; Chiu & Xihua, 2008), but also school level assessments (Engin-Demir, 2009; Ma & Klinger, 2000).Researchers have also indicated that affective factors, such as math self-efficacy and math anxiety, play a crucial role in mathematics achievement. Social learning theorists define perceived self-efficacy as to believe in one's capabilities organize and execute the courses of action required manage prospective situations (Bandura, 1993, p. 2). When this theory is applied subject specific self-efficacy, such as math self-efficacy, children with high math self-efficacy would be likely demonstrate higher achievement in mathematics (Lent, Brown, & Gore, 1997). Furthermore, those who maintain a resilient sense of self-efficacy set challenging goals for themselves, make good use of analytic thinking skills, and have a firmer commitment reach these goals. Having a high level of self-efficacy also regulates and reduces both stress and anxiety (Bandura, 1993; Bandura & Locke, 2003). Math anxiety in the PISA is defined as students' feelings of helplessness and stress when dealing with mathematics (OECD, 2013a). The components of math anxiety were found be similar those of identified in test anxiety (Wigfield & Meece, 1988) with some researchers describing math anxiety as a subject specific test anxiety (Bandalos, Yates, & Thorndike-Christ, 1995; Hembree, 1990). The predictive power of mathematic self-efficacy and math anxiety on students' mathematics achievement has been well documented. Self-efficacy is positively associated with students' academic performance (Alci, Erden, & Baykal, 2010; Chiu & Xihua, 2008; i§ Guzel & Berberoglu, 2010; Lee & Stankov, 2013), whereas anxiety is negatively associated with students' academic performance (Cassady & Johnson, 2002; Hembree, 1990; Ho et. al, 2000; Ma, 1999; Seipp, 1991; McCarthy & Goffin, 2005 ). However, just as math anxiety's effect on mathematics achievement tends be relatively small, its debilitating effect neither direct nor significant (Meece, Wigfield, & Eccles, 1990).At the country level, math self-efficacy and math anxiety are associated with mathematics performance. Across OECD countries, a 28% and 14% variation in students' performance in mathematics can be explained by differences in students' reported levels of math self-efficacy and math anxiety, respectively (OECD, 2013a). At the cross country level however, the relationship between math self-efficacy, anxiety, and achievement is relatively complicated (Bodas & Ollendick, 2005; Liu, 2009). …

Over the last decade, Canadian students have exhibited insubstantial improvements in mathematical scores compared to other countries as indicated by large-scale educational assessments such as the Programme for International Student Assessment (PISA) and the Trends in International Mathematics and Science Study (TIMSS). In relation to students’ mathematical performance, math anxiety - the feeling of fear or nervousness when performing math-related tasks - was found as an associated factor. However, no previous study has explored math performance and math anxiety, specifically among Albertan students. We present a work-in-progress that identifies significant predictors of math performance and math anxiety among Canadian and Albertan students, using the PISA 2018 and TIMSS 2019 datasets. This study has three phases: first, a list of predictors will be selected from the data set based on existing theories regarding students’ math performance and math anxiety. The initial list of predictors will be presented to domain experts (i.e., math teachers) for refinement based on their practical experience. A predictive model for math performance and math anxiety will be developed with Educational Data Mining techniques in the second phase. Results from the model will be presented to the domain experts for their inputs as the qualitative component, and variable importance metrics of the model will be consulted for the quantitative component. Findings from both components will be integrated consulted with the domain experts to derive actionable recommendations that would inform various stakeholders (e.g., educators, school districts, and Alberta Education) of ways to improve math performance in Alberta students.

The Program for International Student Assessment (PISA) has deservedly become the benchmark for comparing national K-12 school systems. Since 2000, the OECD has, at three year intervals, organized PISA “rounds” to assess school system performance in member countries and in non-member partner countries, among upper-secondary students, age 15, in three core subjects. This Commentary summarizes major conclusions relevant to Canada from the latest round, in 2015. The policy recommendation of this Commentary is implicit: educators, administrators and parents should make use of PISA results as a guide to strategic priorities for education policy. Canada’s overall PISA score has consistently ranked well above the OECD average on the three subjects assessed (reading, mathematics, and science). In 2015, Canada ranked 10th in mathematics, 3rd in reading, 7th in science. Overall, our school system is faring well. However, PISA provides ample evidence to prompt some humility among Canadians. To be more specific: • Trends in mathematics: Since the inauguration of PISA, Canadian performance in mathematics has consistently declined from one round to the next, and the gap between 2003 and 2015 results is statistically significant. • Gender gaps: Canada is not faring well on this dimension; it is close to the OECD average. There exist in Canada modest gender gaps in mathematics and science that favour boys. A much larger gender gap in reading favours girls. • Mediocre outcomes for the six small provinces, for Manitoba and Saskatchewan in particular: From the base year for each subject to 2015, PISA score declines in all three subjects have been statistically significant for Manitoba and Saskatchewan. In all three subjects, the levels in these provinces are now below the benchmark year OECD average. There are reasons to speculate that the high proportion of Indigenous students in Manitoba and Saskatchewan is a key factor in explaining their PISA performance. Relative to these two Prairie provinces, outcomes are better in the four Atlantic provinces, but they, too, are not faring well. Each of the four has one 2015 score below 500; among the four, all scores are below the relevant national Canadian average.

The aim of this paper is to analyse the students’ performance in the mathematical competency aspect of the Programme for International Student Assessment (PISA) 2015 tests and to compare 5 Spanish regions in this respect – Navarra, Castile-Leon and Catalonia, with results above the national average; and Extremadura and Andalusia, with results below the national average – in order to identify the factors causing these differences. To do so, we computed the degree of association between variables related to the students and to the schools with the scores obtained in the mathematical tests. The purpose of this analysis was to better understand the meaning of these scores and their causes and, above all, to propose educational policy actions for improving the students’ mathematical performance. In this study, a 2-level regression model was applied to the data collected in the PISA 2015 tests. The first level included the factors related to the students and the second was composed of the variables related to the schools. Our results highlight the significant influence of factors such as immigrant status, grade repetition, location of the school (rural or urban) and economic and sociocultural status. The relevance of these factors to students’ academic performance has been observed in previous editions of the PISA tests. We emphasise the need for action to improve students’ mathematical performance and, therefore, their educational success.Keywords: educational assessment; mathematical comprehension; multilevel analysis; performance factors; PISA; regions

Interpreting mathematics performance in PISA: Taking account of reading performance

This paper employs data from the Program for International Student Assessment (PISA) 2012 study on mathematics performance in Australian secondary schools to determine the effect of mathematics anxiety on mathematics performance among secondary students. Data of school and student specific factors that are relevant to the Australian educational context are extracted from the PISA 2012 study. These data are used to measure the influence of these factors, as well as mathematics anxiety, on students' mathematics performance. Potential predictive factors are also used in the assessment including gender, socio-economic status (SES) and mathematics anxiety. Findings support the existence of an inverse relationship between mathematics performance and mathematics anxiety whereby the influence of mathematics anxiety varies based on students’ gender and SES.

Background: The results of the Programme for International Student Assessment (PISA) 2012 showed that Indonesia, Malaysia, and Thailand underperformed and were positioned in the bottom third out of 65 participating countries for mathematics, science, and reading literacies. The wide gap between these three countries and the top performing countries has prompted this study to examine the influence of students’ affective characteristics on their performance in mathematics literacy using a multilevel analysis. The purpose of this study is to examine the relationships among affective characteristics-related variables at the student level, the aggregated school-level variables, and mathematics performance by using the Programme for International Student Assessment (PISA) 2012 dataset. Method: The data used for the analysis were retrieved from the official PISA website. The student samples from Indonesia, Malaysia and Thailand were 5, 622, 5, 192 and 6, 602, respectively. The data were analysed using descriptive statistics, and a hierarchical linear modeling (HLM) approach with the HLM version 7.0 computer programme. Results: Different patterns of relationships were found between student- and schoollevel variables and mathematics performance in the three countries. The common student-level variable is attitudes towards learning outcomes, which predicted an increase in scores for the Indonesian, Malaysian, and Thai models. At the student level, the strongest predictor on mathematics literacy performance was mathematics self-efficacy for both Indonesian and Malaysian models, and perseverance for the Thai Model. At the school level, school average mathematics self-efficacy was the strongest predictor of mathematics performance in the Indonesian model; average openness to problem-solving in the Thai model; and school average instrumental motivation, mathematics behaviour, and attitudes towards learning outcomes predicted a decrease in scores for the Malaysian model. Conclusion: The inconclusive results of the multilevel analysis has demonstrated some interesting points for discussion, though the results could be attributed to the differences in education system and a diversity of cultural context in each individual country. This study contributes to providing evidence-based policy making in addition to informing the mathematics teachers the particular students’ affective characteristics, which should be strengthened to ensure better mathematics learning outcomes in Indonesia, Malaysia, and Thailand. Implications of the findings and limitations are discussed.

This study investigates the influence of Information and Communication Technology (ICT) on students' performance in mathematics across six South-East Asian countries, utilizing the Programme for International Student Assessment (PISA) data. The research focuses on data from the 2018 PISA wave and includes six countries: Brunei Darussalam, Indonesia, Malaysia, the Philippines, Singapore, and Thailand. A multivariate linear regression analysis reveals that several factors significantly impact mathematics achievement, including economic, social, and cultural status (ESCS); the proportion of female students; and ICT-related variables such as computer availability and internet connectivity. Results indicate that higher ESCS and a greater proportion of female students correlate positively with higher PISA mathematics scores. The findings suggest that enhancing ICT resources in schools, as well as improving ICT-related home possessions, can lead to better educational outcomes in mathematics. This research highlights the critical role of ICT in fostering academic achievement and calls for policies that prioritize ICT integration in education.

This study examined the effects of opportunity to learn (OTL) or the content coverage in mathematics on student mathematics anxiety, problem-solving performance, and mathematics performance. The pathways examining the influences of OTL on student problem-solving performance and mathematics performance via mathematics anxiety were also tested. A sample of 1,676 students from Shanghai-China, and a sample of 1,511 students from the United States who participated in the Programme for International Student Assessment (PISA) 2012 were used for the analyses. The results from multilevel models and path models supported our hypotheses that OTL not only showed significant direct effects on student mathematics anxiety, problem-solving performance, and mathematics performance, but also presented indirect effects on student problem-solving performance and mathematics performance via mathematics anxiety in both Shanghai-China and United States, controlling for student gender, grade, and socioeconomic status. The practical implications of the current results were also discussed.

As an effort to account for disparities in mathematics performance between American and East Asian middle school students, the present research aims to compare the relationship between learning styles (competitive and cooperative) and mathematics performance among middle school students between the USA and the three top-performing East Asian countries (Hong Kong, Japan, and Korea) in 2003 Programme for International Student Assessment (PISA). Results from hierarchical linear model (HLM) with students nested within schools demonstrated three key findings: (a) competitive learning had a statistically significant positive though small relationship with mathematics performance in all four countries, (b) cooperative learning had a statistically significant positive though small relationship with mathematics performance in the three East Asian countries but not in the USA, and (c) the relationship between competitive learning and mathematics performance was as strong as the relationship between cooperative learning and mathematics performance across the three East Asian countries. Although American students are stronger competitive and cooperative learners than their East Asian peers, they are not effective users of either learning style for the improvement of mathematics performance likely because of the way that both learning styles are practiced in American mathematics classrooms. Teacher education may hold the key to improve the educational practice of different learning styles as a strategy to improve mathematics performance of students in the USA and beyond.

This chapter starts with a review of the development of the concept of mathematical literacy proposed by the Programme for International Student Assessment (PISA) followed by an analysis of the changes in the mathematics assessment framework across cycles. Along with devoting more attention to noncognitive skills in the field of education, PISA developed mathematics-related measures on attitudes and emotions that contribute to students using and developing their mathematical capacities. The participation of China Mainland in PISA began with Shanghai in 2009 and 2012. During these two appearances, the performance of Shanghai topped the mathematics achievement ladder, which produces a global ‘PISA-shock’. However, PISA found that Shanghai students’ self-concept and mathematics intentions are relatively low, while both self-belief and dispositions to mathematics show significantly positive impacts on mathematics performance. Regarding the rich information from the PISA studies, both education officials and the public in China Mainland gave generally reflective, measured and self-critical responses.

Stratification is an important design feature of many studies using complex sampling designs and it is often used in large-scale assessment (LSA) studies, such as the Programme for International Student Assessment (PISA), for two main reasons. First, stratification variables that achieve a high between and low within strata variance can improve the efficiency of a survey design. Second, stratification allows one to, explicitly or implicitly, control for sample sizes across subpopulations. It ensures that some parts of a population are in the sample in predetermined proportions. In this study, we determine through simulation which stratification scheme is best for PISA in Germany. For this, we consider the constraints imposed by the international sampling design, the available information about schools, and specific national characteristics of the German educational system. We examine seven different stratification designs selected based on scenarios used in past LSAs in Germany and theoretical considerations for future implementations. The chosen scenarios were compared with two reference scenarios: (1) an unstratified design and (2) a synthetic optimal stratification design. The simulation study reveals that the stratification design currently applied in PISA produces satisfactory results regarding sampling precision. The present stratification design is based on Germany's federal states and school types. However, this approach leads to small strata, which has been problematic for estimating sampling variance in previous cycles. Therefore, alternative stratification scenarios were considered and, in addition to overcoming the small-strata problem, also led to smaller standard errors for estimates of student mean performance in mathematics, science, and reading. As a result of this study, we recommend considering three different stratification designs for Germany in future cycles of PISA. These recommendations aim to: (1) improve the sampling efficiency while keeping the sample size constant, (2) follow a sound methodological approach, and (3) make conservative and cautious changes while maintaining a reflection of the structure of the German federal school system with different school types. These suggestions include a reinvented stratification of grouped German federal states and designs with school types as explicit stratifiers and federal states as implicit stratifiers.

This study examined over 48,000 eighth-grade Chinese students’ mathematics performance and use of learning strategies. In particular, this study explored how the use of learning strategies and their combinations are related to students’ mathematics performance in Chinese context. This study examined three kinds of learning strategies derived from the Programme for International Student Assessment (PISA) 2012: memorization, elaboration, and control. The findings indicate that few students chose the memorization strategy as their predominant way of learning mathematics. Instead, students usually chose a combination of learning strategies. The choice of combinations of learning strategies was highly correlated with students’ mathematics performance. Students who chose control strategies outperformed those who chose memorization strategies or elaboration strategies. This study also examined the relationship between learning strategy use and mathematics performance of students who were only children (students with no siblings, born under the “one child” policy) and students living in nonagricultural residential locations (hukou status), both of which are background variables unique to Chinese context. The findings indicate that only-child and nonagricultural students outperformed non-only-child and agricultural students when choosing the same learning strategies. However, those who chose control-dominant strategies performed better than those who chose memorization-dominant strategies, regardless of their background. This study provides a new lens for considering the relationship between learning strategy use and mathematics performance, demonstrating that various learning strategies should be considered together rather than separately.

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