IEA INSIDER 2025
Lene Nors Nielsen earned a Master’s degree in Educational Sociology from the Danish School of Education (DPU), Aarhus University in 2024. She has 13 years of experience teaching mathematics and science in Danish lower secondary schools (grades 7–9). In spring 2023, she participated in the team responsible for coordinating the data collection for the TIMSS and ICILS studies in Danish schools. She is currently employed as a data and analysis consultant at the Municipality of Copenhagen.
regression models and using variable selection techniques for dimension reduction. Results showed that this approach can preserve or even improve measurement precision while using far fewer variables. The third study investigates gender differences in reading in the context of girls consistently outperforming boys on average across countries and assessment cycles. Using PIRLS 2021 data from four countries, the study identified three behavior types: “Rapid,” “Challenged,” and “Engaged.” Boys were more likely to be in the Rapid class, while more girls belonged to the Engaged class. Within the Rapid class, girls on average outperformed boys across countries, while no gender differences were observed in reading achievement in the Engaged class in three of the four countries. Further analyses suggested that the advantage of girls over boys in average reading achievement may diminish to a mild to moderate extent if the boy population adopted test-taking behaviors similar to girls. Together, these studies demonstrate that process data can provide insights beyond what traditional response and background data can offer, particularly in uncovering students’ test-taking behaviors, explaining achievement gaps, and advancing psychometric methodologies. Dr. Dihao Leng is a Senior Research Specialist at the TIMSS & PIRLS International Study Center at Boston College, specializing in advanced statistics, psychometrics, and AI applications. She earned her PhD in Measurement, Evaluation, Statistics, and Assessment from Boston College and previously interned at the Graduate Management Admission Council. She also holds a Master’s degree in Mathematics Education and a Bachelor’s degree in Mathematics and Applied Mathematics from Fudan University, China. ■
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Does Process Data Add Value to the Analysis of International Large- Scale Assessment Data? As international large- scale assessments (ILSAs) transition from paper to computer-based formats, they
now record how students interact with the test, such as how quickly students respond and how they navigate through the test. This type of data, known as “process data,” has the potential to shed light on students’ test-taking behavior. In the meantime, collecting and analyzing process data involves considerable costs and complexity, and raises concerns around privacy and data accountability. This prompts a fundamental question: Is process data truly useful? This dissertation responds to the question through three studies using anonymized process data. The first uses TIMSS 2019 data to understand gender differences in test-taking behavior and their relationships with mathematics achievement. The study found that boys, on average, answered questions faster than girls across all 10 countries studied. Furthermore, slower response speed was associated with higher achievement. In contrast, students’ propensity to revisit questions was not strongly related to either achievement or response speed. The study also proposed a multiple-group speed-accuracy-revisits model that can be applied to examine group differences beyond gender. The second study focuses on methodological improvements for achievement estimation in ILSAs. The conventional approach uses principal component analysis to reduce the number of background variables before latent regression modeling. However, this approach often retains more variables than ideal, which can lead to overparameterization and threaten model stability. This study proposes a new approach: incorporating process variables into latent
IEA offers two annual awards to recognize high-quality empirical research that makes use of IEA data.
These awards were established in commemoration of the significant contributions that Bruce H. Choppin and Richard M. Wolf made to IEA.
The IEA Research Awards are held annually and the deadline for applications is the 31 March of each year.
Find out more via iea.nl/about/opportunities/award
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