Big Data Meta-Analyses of The Digital Gender Divide Among Students in Elementary and Secondary Education

• Applicant: Professor Dr. Martin Brunner
• Subject Area: General and Domain-Specific Teaching and Learning
• Term: since 2020
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 442358899

Digital transformation and advances in information and communication technologies (ICT) are having an enormous impact on economies and societies. However, there is a digital gender divide in ICT that manifests itself in four key dimensions: access, motivation, competence, and use. The main goal of this project is to provide highly robust and widely generalizable knowledge on the size, variability, and moderating factors of cross-national gender differences in the four ICT-related dimensions among students in elementary and secondary education. These findings will allow us to identify plausible antecedents that may help explain why many women cannot (or will not) participate in the digital world as much as men. Specifically, we will meta-analyze ICT-related individual student data from over 1,000 representative samples participating in four international large-scale assessments covering the period from 1995 to 2022: the trends in International Mathematics and Science Study (TIMSS), the Progress in International Reading Literacy Study (PIRLS), the International Computer and Information Literacy Study (ICILS), and the Program for International Student Assessment (PISA). This project will be the first to quantitatively synthesize this wealth of Big Data with meta-analytic methods. In particular, we will conduct several meta-analyses to examine gender differences in students’ ICT-related access, motivation, competence, and use. We will study students’ age and socioeconomic status (SES), the selectivity of the sample (e.g., the bottom or top 5% of the competence distribution), sociocultural indicators of gender equality, and time of measurement as moderators of gender differences. In addition, we will conduct a meta-analysis to examine ICT-related gender differences among top-performing students who score in the top 5% in mathematics or ICT-competence in their respective countries. In summary, our project will provide novel insights into cross-national, temporal, and age-related trends concerning ICT-related gender differences in the general student population and among top-performing students, as well as into the complex interactions between gender, sample selectivity, SES, and sociocultural indicators of gender equality. This knowledge is highly relevant, as it can be used to (1) learn about gender differences before university entry as plausible antecedents of gender gaps in ICT-related studies and occupations, (2) provide scientific evidence that can help dispel persistent stereotypes (e.g., that only boys can excel in programming) that may discourage girls from pursuing ICT-related careers, and (3) identify target groups and points for evidence-based interventions in educational policy (e.g., girls from families with low SES).

• DFG Programme: Research Grants
• International Connection: USA
• Co-Investigators: Professor Dr. Oliver Lüdtke; Professorin Dr. Franzis Preckel
• Cooperation Partner: Professor Larry V. Hedges, Ph.D.