scholarly journals The PISA performance gap between national and expatriate students in the United Arab Emirates

2020 ◽  
Author(s):  
Jose Marquez ◽  
Louise Lambert ◽  
Natasha Ridge ◽  
Stuart Walker
Keyword(s):  
Public Schools ◽  
United Arab Emirates ◽  
Private Schools ◽  
International Student ◽  
Linear Regression Analysis ◽  
Student Assessment ◽  
School Type ◽  
International Student Assessment ◽  
Immigrant Background ◽  
Using Data

In most education systems, students with an immigrant background perform worse academically compared to native students. However, in the United Arab Emirates (UAE), differences emerge in the opposite direction and the national-expatriate gap in academic competence is equivalent to almost three years of schooling. This gap is a concern in the UAE, where national students mainly attend public schools and expatriates, mostly private schools. To investigate the competence gap between national and expatriate students, we estimate group differences and conduct linear regression analysis using data from the 2018 Programme for International Student Assessment. Results show that the gap varies by emirate and country of origin and is greater among boys, better-off students and in private schools. Between 33% and 47% of this gap is explained by school type, whether public or private. We offer recommendations; however, in a country characterized by 85% expatriates and a maturing education policy, challenges remain, but may serve to pave the way for other high expatriate nations in development.

scholarly journals Determinants of Declining School Belonging 2000–2018: The Case of Sweden

2021 ◽  
Author(s):  
Björn Högberg ◽  
Solveig Petersen ◽  
Mattias Strandh ◽  
Klara Johansson
Keyword(s):  
Education Reform ◽  
School Environment ◽  
International Student ◽  
Student Assessment ◽  
School Belonging ◽  
Foreign Born ◽  
Bottom Part ◽  
Performance Orientation ◽  
International Student Assessment ◽  
Using Data

AbstractStudents’ sense of belonging at school has declined across the world in recent decades, and more so in Sweden than in almost any other high-income country. However, we do not know the characteristics or causes of these worldwide trends. Using data on Swedish students aged 15–16 years from the Programme for International Student Assessment (PISA) between 2000 and 2018, we show that the decline in school belonging in Sweden was driven by a disproportionately large decline at the bottom part of the distribution, and was greatest for foreign-born students, students from disadvantaged social backgrounds, and for low-achieving students. The decline cannot be accounted for by changes in student demographics or observable characteristics related to the school environment. The decline did, however, coincide with a major education reform, characterized by an increased use of summative evaluation, and an overall stronger performance-orientation.

Fabrication of interview data

2018 ◽  
Vol 26 (2) ◽  
pp. 213-226 ◽  
Author(s):  
Jörg Blasius
Keyword(s):  
Survey Research ◽  
International Student ◽  
Student Assessment ◽  
Content Type ◽  
International Student Assessment ◽  
Pisa 2012 ◽  
Research Organizations ◽  
Using Data ◽  
Copy And Paste ◽  
Practical Implications

Purpose Evidence from past surveys suggests that some interviewees simplify their responses even in very well-organized and highly respected surveys. This paper aims to demonstrate that some interviewers, too, simplify their task by at least partly fabricating their data, and that, in some survey research institutes, employees simplify their task by fabricating entire interviews via copy and paste. Design/methodology/approach Using data from the principal questionnaires in the Programme for International Student Assessment (PISA) 2012 and the Programme for the International Assessment of Adult Competencies (PIAAC) data, the author applies statistical methods to search for fraudulent methods used by interviewers and employees at survey research organizations. Findings The author provides empirical evidence for potential fraud performed by interviewers and employees of survey research organizations in several countries that participated in PISA 2012 and PIAAC. Practical implications The proposed methods can be used as early as the initial phase of fieldwork to flag potentially problematic interviewer behavior such as copying responses. Originality/value The proposed methodology may help to improve data quality in survey research by detecting fabricated data.

scholarly journals The Influence of Family Wealth and Parent Education Level on Students� Reading Literacy

2019 ◽  
Vol 9 (1) ◽  
pp. 81-90
Author(s):  
Abu Nawas
Keyword(s):  
Regression Analysis ◽  
Parent Education ◽  
International Student ◽  
Family Background ◽  
Linear Regression Analysis ◽  
Student Assessment ◽  
Reading Literacy ◽  
International Student Assessment ◽  
Family Wealth ◽  
Education Levels

This study aims to examine the influence of family background factors in terms of family wealth and parent education levels on students reading performance in Indonesia. The study utilises secondary data from the OECDs Programme for International Student Assessment (PISA) 2015 for Indonesia, in which 6513 students participated. This also specifically highlights the analysis of family wealth and parent education levels in possibly predicting the students reading literacy in Indonesia. In analysing the data, a quantitative approach was used which utilised statistically different analysis such as t-test, one-way ANOVA, two-way ANOVA, correlation and multiple linear regression analysis using WesVar version 5.1 software.The result found there were significant different reading scores between students from different family wealth and parent education levels. The students from high family wealth performed better than they with middle and low wealthy. Likewise, the children with highly educated mother and father had high scores than students whose parents had low and did not complete primary school. Moreover, the result of correlation and regression analysis revealed that all predictor variables, WEALTH, MISCED and FISCED, significantly associate and predict better reading literacy performance of 15-year-old students in Indonesia for PISA 2015 survey. Therefore, the implications of the study highlight opportunities to reform educational policies through data and evidence.

scholarly journals Evidence and Promises of AI Predictions to Understand Student Approaches to Math Learning in Abu Dhabi K12 Public Schools

2021 ◽  
Author(s):  
Xin Miao ◽  
Pawan Kumar Mishra ◽  
Ali Nadaf
Keyword(s):  
Public Schools ◽  
Test Performance ◽  
Student Behavior ◽  
International Student ◽  
Student Assessment ◽  
Abu Dhabi ◽  
Metacognitive Skills ◽  
Math Learning ◽  
National Education ◽  
International Student Assessment

Transforming the education system and building highly skilled human capital for a sustainable and competitive knowledge economy have been on the UAE’s top policy agendas for the last decade. However, in the UAE, students’ math performance on the Program for International Student Assessment (PISA) has not been promising. To improve the quality of schooling, a series of malleable predictive factors including the contributions of self-system, metacognitive skills, and instructional language skills are selected and categorized under student approaches to math learning. These factors are hypothesized as both predictors and outcomes of K12 schooling. Through the analysis using machine learning technique, XGBoost, a latent relationship between student approaches to math learning and math diagnostic test performance is uncovered and discussed for students from Grade 5 to Grade 9 in Abu Dhabi public schools. This article details how the analysis results are applied for student behavior and performance prediction, precise diagnosis, and targeted intervention design possibilities. The main purpose of this study is to diagnose challenges that hinder student math learning in Abu Dhabi public schools, uncover R&D initiatives in AI-driven prediction and EdTech interventions to bridge learning gaps, and to counsel on national education policy refinement.

scholarly journals Educational Achievement Gaps between Immigrant and Native Students in Two “New” Immigration Countries

2012 ◽  
Vol 643 (1) ◽  
pp. 46-77 ◽  
Author(s):  
Davide Azzolini ◽  
Philipp Schnell ◽  
John R. B. Palmer
Keyword(s):  
International Student ◽  
Immigrant Students ◽  
Educational Achievement ◽  
Student Assessment ◽  
Achievement Gaps ◽  
Empirical Literature ◽  
Socioeconomic Background ◽  
School Type ◽  
Native Students ◽  
International Student Assessment

The authors use 2009 Programme for International Student Assessment (PISA) data to determine how immigrant children in Italy and Spain compare with native students in reading and mathematics skills. Drawing on the vast empirical literature in countries with traditionally high rates of immigration, the authors test the extent to which the most well-established patterns and hypotheses of immigrant/native educational achievement gaps also apply to these comparatively “new” immigration countries. The authors find that both first- and second-generation immigrant students underperform natives in both countries. Although socioeconomic background and language skills contribute to the explanation of achievement gaps, significant differences remain within the countries even after controlling for those variables. While modeling socioeconomic background reduces the observed gaps to a very similar extent in both countries, language spoken at home is more strongly associated with achievement gaps in Italy. School-type differentiation, such as tracking in Italy and school ownership in Spain, do not reduce immigrant/native gaps, although in Italy tracking is strongly associated with immigrant students’ test scores.

Socioeconomic status and wellbeing as predictors of students’ academic achievement: evidence from a developing country

2021 ◽  
pp. 1-19
Author(s):  
Cahit Erdem ◽  
Metin Kaya
Keyword(s):  
Academic Achievement ◽  
Socioeconomic Status ◽  
Developing Country ◽  
International Student ◽  
Linear Regression Analysis ◽  
Student Assessment ◽  
Multiple Linear Regression Analysis ◽  
Cross Sectional ◽  
Independent Variables ◽  
International Student Assessment

Abstract The COVID-19 pandemic has deepened the effects of socioeconomic status (SES) and wellbeing (WB) on students’ academic achievement, particularly in developing countries; thus, it becomes necessary to understand the nature of these concurrent relationships. This study aimed to explore the relationships between SES, WB and academic achievement, based on the data from the Programme for International Student Assessment (PISA) in 2018 within the Turkish context. In this cross-sectional study, we used hierarchical multiple linear regression analysis to explore how the independent variables predicted academic achievement in blocks based on data from 6890 students attending 186 schools. The study revealed that the model, including the independent variables, predicted students’ achievement in reading, mathematics and science; however, the prediction level of demographic factors and domains of WB were very low, while SES had the highest prediction level. The results offer insights into the predictors of academic achievement and educational inequalities in the context of a developing country.

Moving beyond Country Rankings in International Assessments: The Case of PISA

2015 ◽  
Vol 117 (1) ◽  
pp. 1-10
Author(s):  
Nancy Perry ◽  
Kadriye Ercikan
Keyword(s):  
Teaching And Learning ◽  
International Student ◽  
Research Policy ◽  
Student Assessment ◽  
Approaches To Learning ◽  
International Student Assessment ◽  
International Assessments ◽  
Motivation For Learning ◽  
Using Data ◽  
Pisa Data

The Programme for International Student Assessment (PISA) was designed by the Organisation for Economic Cooperation and Development (OECD) to evaluate the quality, equity, and efficiency of school systems around the world. Specifically, the PISA has assessed 15-year-old students’ reading, mathematics, and science literacy on a 3-year cycle, since 2000. Also, the PISA collects information about how those outcomes are related to key demographic, social, economic, and educational variables. However, the preponderance of reports involving PISA data focus on achievement variables and cross-national comparisons of achievement variables. Challenges in evaluating achievement of students from different cultural and educational settings and data concerning students’ approaches to learning, motivation for learning, and opportunities for learning are rarely reported. A main goal of this themed issue of Teachers College Record (TCR) is to move the conversation about PISA data beyond achievement to also include factors that affect achievement (e.g., SES, home environment, strategy use). Also we asked authors to consider how international assessment data can be used for improving learning and education and what appropriate versus inappropriate inferences can be made from the data. In this introduction, we synthesize the six articles in this issue and themes that cut across them. Also we examine challenges associated with using data from international assessments, like the PISA, to inform education policy and practice within and across countries. We conclude with recommendations for collecting and using data from international assessments to inform research, policy, and teaching and learning.

scholarly journals Trends in International PISA Scores over Time: Which Countries Are Actually Improving?

2019 ◽  
Vol 8 (8) ◽  
pp. 231 ◽  
Author(s):  
Kristie J. Rowley ◽  
Shelby M. McNeill ◽  
Mikaela J. Dufur ◽  
Chrisse Edmunds ◽  
Jonathan A. Jarvis
Keyword(s):  
International Student ◽  
Student Assessment ◽  
Accurate Assessment ◽  
Policy Decisions ◽  
International Student Assessment ◽  
Country Level ◽  
Form Of Government ◽  
Using Data ◽  
Over Time

Many countries attempt to increase their Program for International Student Assessment (PISA) rankings and scores over time. However, despite providing a more accurate assessment of the achievement-based improvements across countries, few studies have systematically examined growth in PISA scores over multiple assessments. Using data from the 2006, the 2009, and the 2012 PISA, we analyzed which countries experienced significant increases in their country-level average PISA scores between 2006 and 2012. To facilitate improved policy decisions, we also examined what country-level conditions were associated with such increases. Contrary to expectations, we found that few countries significantly increased their PISA scores over time. Countries that did experience meaningful improvements in PISA scores were more likely to have had lower PISA scores in 2006 and experienced country-level foundational advancements more recently, such as advancing to a more democratic form of government and/or a higher income classification.

Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs

2017 ◽  
Vol 43 (3) ◽  
pp. 316-353 ◽  
Author(s):  
Simon Grund ◽  
Oliver Lüdtke ◽  
Alexander Robitzsch
Keyword(s):  
Missing Data ◽  
Multiple Imputation ◽  
International Student ◽  
Student Assessment ◽  
Joint Modeling ◽  
Computational Procedure ◽  
International Student Assessment ◽  
Fully Conditional Specification ◽  
Using Data ◽  
Level 2

Multiple imputation (MI) can be used to address missing data at Level 2 in multilevel research. In this article, we compare joint modeling (JM) and the fully conditional specification (FCS) of MI as well as different strategies for including auxiliary variables at Level 1 using either their manifest or their latent cluster means. We show with theoretical arguments and computer simulations that (a) an FCS approach that uses latent cluster means is comparable to JM and (b) using manifest cluster means provides similar results except in relatively extreme cases with unbalanced data. We outline a computational procedure for including latent cluster means in an FCS approach using plausible values and provide an example using data from the Programme for International Student Assessment 2012 study.

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