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The role of students’ non-cognitive factors and school resources in predicting mathematics achievement using PISA 2018 Indonesia data
Abstract
Many factors affect students’ mathematics achievement. In addition to cognitive factors, many studies also highlight and show that non-cognitive factors of students and school resources become important factors in influencing students’ mathematical achievement. This study analyzed the relationship between students’ non-cognitive factors and school resources to mathematics achievement. The data were taken from 9,620 of the 338 schools in Indonesia involved in participating in the 2018 Program for International Student Assessment (PISA). The results of the multilevel analysis found that the students’ happy feelings, the students’ cooperation and the students’ belief were a statistically significant relationship to mathematics achievement with all positive relationships. The low quality of educational materials and teaching staff in schools were statistically significant and negatively correlated with mathematics achievement. This study showed that the non-cognitive factors and low quality of teaching staff had an important role for students in achieving mathematics.
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This research aims at knowing the factors, involving both students and school levels, related to the math learning achievement for students in Indonesia, Japan, and Algeria by using PISA 2015 data. The sample in this study consists of students from three countries that took part in PISA 2015. The three countries chosen are Indonesia, Japan, and Algeria, each respectively having as participants 5.800, 6.411, and 4.460. the findings showed that the sense of belonging of students towards mathematics, the socio-economic status of their families, and the average of school’s social-economic status can predict significantly the students’ math learning achievement for the Indonesia and Japan, while for the Algerian students the socio-economic status is statistically insignificant in predicting their math learning achievement. The outcome of this analysis support the idea that the school attended plays a big role as far as mathematics learning achievement is concerned. To conclude, it should be summed up that the affective characteristics (sense of belonging of students), family background (students’ socio-economic status), and the variable school-level (average socio-economic status of schools) can be among students as far as mathematics learning achievement is concerned.
Recent evidence about the relationship between cognitive tests and psychological noncognitive variables is reviewed. Noncognitive measures can be ordered with respect to their predictive validity. Many are poor predictors of intelligence and achievement. Measures of rationality, self-assessment of intelligence, Openness to Experience and self-concept correlate up to .35 with cognitive performance. Some domain-specific self-beliefs (self-efficacy and anxiety) have correlations with appropriate achievement tests that can reach .45. The best predictors of any kind of cognitive performance are measures of confidence (frequently reported correlations of .45 and above) that can capture a major part of predictive validity of the three self-beliefs. The role of self-beliefs has attracted much interest in education but their role in predicting performance on tests of fluid intelligence is likely to be low. However, self-beliefs and confidence in particular may prove to be the most potent noncognitive influences on the development of acculturated knowledge that is captured by measures of crystallized intelligence.
Emphasizing the developmental need for positive peer relationships, in this study the authors tested a social-contextual view of the mechanisms and processes by which early adolescents' achievement and peer relationships may be promoted simultaneously. Meta-analysis was used to review 148 independent studies comparing the relative effectiveness of cooperative, competitive, and individualistic goal structures in promoting early adolescents' achievement and positive peer relationships. These studies represented over 8 decades of research on over 17,000 early adolescents from 11 countries and 4 multinational samples. As predicted by social interdependence theory, results indicate that higher achievement and more positive peer relationships were associated with cooperative rather than competitive or individualistic goal structures. Also as predicted, results show that cooperative goal structures were associated with a positive relation between achievement and positive peer relationships. Implications for theory and application are discussed.
This study uses Early Childhood Longitudinal Study data to investigate the importance of three general aspects of teacher effects—teacher background qualifications, attitudes, and instructional practices—to reading and math achievement gains in first grade. The results indicate that compared with instructional practices, background qualifications have less robust associations with achievement gains. These findings suggest that the No Child Left Behind Act’s “highly qualified teacher” provision, which screens teachers on the basis of their background qualifications, is insufficient for ensuring that classrooms are led by teachers who are effective in raising student achievement. To meet that objective, educational policy needs to be directed toward improving aspects of teaching, such as instructional practices and teacher attitudes.
Meta-analyses indicated that cooperation is more effective than interpersonal and individual efforts in promoting achievement and productivity, that cooperation in intergroup competition is superior to interpersonal and individual efforts in promoting achievement and productivity, and that interpersonal competition and individual efforts do not differ in effects on achievement and productivity.
The main aim of the present study was to carry out an in-depth examination of 15-year-olds’ mathematics performance in Greece. By applying a multilevel model to the Programme for International Student Assessment 2012 data for Greece, this study investigated the factors, both at individual and school level, which were linked to mathematics achievement. The results revealed that gender, pre-primary education, self-beliefs about mathematics and individual and school mean socioeconomic status can statistically significantly predict students’ mathematics achievement. The analysis also indicated the importance of the school which students attend in shaping their mathematics performance. Generally, it could be concluded that background characteristics, self-constructs and school level variables can explain a large proportion of the variance in students’ mathematic achievement.
Collecting data from students within classrooms or schools, and collecting data from students on multiple occasions over time, are two common sampling methods used in educational research that often require multilevel modeling (MLM) data analysis techniques to avoid Type-1 errors. The purpose of this article is to clarify the seven major steps involved in a multilevel analysis: (1) clarifying the research question, (2) choosing the appropriate parameter estimator, (3) assessing the need for MLM, (4) building the level-1 model, (5) building the level-2 model, (6) multilevel effect size reporting, and (7) likelihood ratio model testing. The seven steps are illustrated with both a cross-sectional and a longitudinal MLM example from the National Educational Longitudinal Study (NELS) dataset. The goal of this article is to assist applied researchers in conducting and interpreting multilevel analyses and to offer recommendations to guide the reporting of MLM analysis results.
In an effort to improve the quality of schools, governments around the world have dramatically increased the resources devoted to them. By concentrating on inputs and ignoring the incentives within schools, the resources have yielded little in the way of general improvement in student achievement. This paper provides a review of the US and international evidence on the effectiveness of such input policies. It then contrasts the impact of resources with that of variations in teacher quality that are not systematically related to school resources. Finally, alternative performance incentive policies are described. Copyright Royal Economic Society 2003
Teacher quality is widely believed to be important for education, despite little evidence that teachers' credentials matter for student achievement. To accurately measure variation in achievement due to teachers' characteristics-both observable and unobservable-it is essential to identify teacher fixed effects. Unlike previous studies, I use panel data to estimate teacher fixed effects while controlling for fixed student characteristics and classroom specific variables. I find large and statistically significant differences among teachers: a one standard deviation increase in teacher quality raises reading and math test scores by approximately .20 and .24 standard deviations, respectively, on a nationally standardized scale. In addition, teaching experience has statistically significant positive effects on reading test scores, controlling for fixed teacher quality.
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