An IERI – International Educational Research Institute Journal
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This empirical study aims to investigate the association between mathematics self-efficacy and mathematics achievement gaps among students in Grades 4, 8, and 12, utilizing data from the 2019 National Assessme...
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 Assessmen...
Self-efficacy or perceived ability refers to the confidence an individual has in their ability to successfully perform a specific task. Previous researchers have demonstrated the importance of self-efficacy be...
As the use of process data in large-scale educational assessments is becoming more common, it is clear that data on examinees’ test-taking behaviors can illuminate their performance, and can have crucial ramif...
Large scale international assessments depend on invariance of measurement across countries. An important consideration when observing cross-national differential item functioning (DIF) is whether the DIF actua...
Learning strategies have been recognized as important predictors of mathematical achievement. In recent studies, it has been found that Asian students use combined learning strategies, primarily including meta...
Artificial intelligence (AI) is rapidly changing communication and technology-driven content creation and is also being used more frequently in education. Despite these advancements, AI-powered automated scori...
This paper scrutinizes the increasing trend of using international large-scale assessment (ILSA) data for causal inferences in educational research, arguing that such inferences are often tenuous. We explore t...
In this journal, Sciffer et al. (Large-scale Assessments in Education 10:1–22, 2022), hereafter SP&M, conclude that school socioeconomic compositional (SEC) or school socioeconomic status (school-SES) effects ...
The use of large-scale assessments (LSAs) in education has grown in the past decade though analysis of LSAs using multilevel models (MLMs) using R has been limited. A reason for its limited use may be due to t...
The benefits of incorporating process information in a large-scale assessment with the complex micro-level evidence from the examinees (i.e., process log data) are well documented in the research across large-...
The Covid-19 pandemic has triggered a rapid and unprecedented transformation of global educational systems. The research community has uncovered important impacts of the pandemic on education worldwide, includ...
Meta-analyses and international large-scale assessments (ILSA) are key sources for informing educational policy, research, and practice. While many critical research questions could be addressed by drawing evi...
Test-taking behavior is a potential source of construct irrelevant variance for test scores in international large-scale assessments where test-taking effort, motivation, and behaviors in general tend to be co...
This paper aims to showcase the value of implementing a Bayesian framework to analyze and report results from international large-scale assessments and provide guidance to users who want to analyse ILSA data u...
In this study we use data from the Programme for International Student Assessment (PISA) to investigate the effect of teachers’ motivation on students’ scientific literacy and motivation in Colombia. These rel...
2022 Citation Impact
3.1 - 2-year Impact Factor
4.0 - 5-year Impact Factor
1.623 - SNIP (Source Normalized Impact per Paper)
0.996 - SJR (SCImago Journal Rank)
2023 Speed
31 days submission to first editorial decision for all manuscripts (Median)
324 days submission to accept (Median)
2023 Usage
418,474 downloads
133 Altmetric mentions
Large-scale Assessments in Education is fully sponsored by IERI – International Educational Research Institute, and the International Association for the Evaluation of Educational Achievement (IEA) that focuses on improving the science of large-scale assessments. Authors do not need to pay an article-processing charge.