In a world increasingly shaped by digital technologies, the development of computer and information literacy (CIL) has emerged as a central focus in education and represents a shared objective across Europe (European Commission, 2020). Digital technologies are now integrated into nearly all areas of life, including education (Fraillon, 2024).

CIL refers to an individual’s ability to use computers for accessing information, creating outputs and communicating effectively across different domains of everyday life (Eickelmann et al., 2024a). Its theoretical development is formed by foundational prerequisites and processes that operate across four different levels (Eickelmann et al., 2024b). Key family characteristics, including (a) social origin and (b) migration background, play a significant role in shaping CIL. At the individual level, factors such as (c) gender, (d) computer experience and (e) self-efficacy are equally pivotal. These elements interact in complex ways, reflecting the multifaceted and interconnected processes underlying the development of CIL (Eickelmann et al., 2024b).

As these processes unfold, significant disparities in CIL can be observed between different student groups (Hatlevik et al., 2015). Bourdieu argues that socioeconomic status (SES) is reflected in performance-related aspects of life, with high SES linked to superior performance due to parents' greater willingness to invest in their children’s futures (Bourdieu, 1973).

Educational research often categorizes students based on various criteria, with (a) SES being a widely utilized determinant. One common indicator of SES is cultural capital, typically measured by the number of books in the household (Eickelmann et al., 2024b; Frohn, 2020; Hatlevik & Christophersen, 2013). Empirical evidence consistently shows that students' competencies vary with SES (Trautwein & Baeriswyl, 2007). Recent studies indicate that low SES generally restricts access to digital media (Vodafone Foundation, 2025). Students from lower SES backgrounds tend to exhibit lower levels of CIL compared to their higher SES counterparts (Hietajärvi et al., 2024). The International Computer and Information Literacy Study (ICILS 2023), conducted by the International Association for the Evaluation of Educational Achievement (IEA), investigates differences in CIL among students (Fraillon, 2024). The study highlights inequities in access to and effective use of digital technologies, providing a comprehensive assessment of students' CIL across diverse SES.

In the same way (b) migration status has been shown to influence CIL, with immigrant students often achieving lower competency levels (Bachmann et al., 2022). (c) Gender is also an influencing factor for CIL. Findings from ICILS 2023 reveal that female students outperform male students in CIL assessments (Eickelmann et al., 2024a). Additionally, research indicates that (d) computer experience constitutes a significant determinant in the development of digital skills (Eickelmann et al., 2024b). In this context, SES serves as a key determinant in shaping students' (e) self-efficacy, which significantly influences their digital literacy development (Seaton et al., 2010). Consequently, these SES differences can lead to a digital divide, defined as the disparity in digital participation between individuals with low and high SES (OECD, 2001).

Despite the increasing integration of digital technologies into education, significant gaps remain in understanding the extent to which socioeconomic disparities influence CIL across European contexts. Our contribution to ECER lies in offering empirical evidence on the intersection of SES and CIL, identifying key individual-level factors influencing disparities, and advancing the understanding of digital inequalities among European students.

In alignment with the aims of ICILS 2023 to investigate such disparities, this contribution addresses these gaps by investigating the following research questions: (I) What is the current situation of students’ CIL differentiated by individual-level factors such as socioeconomic status, migration background, gender, computer experience and self-efficacy in European countries, and (II) to what extent are these factors related to the students’ CIL in European countries?

This contribution is based on a secondary analysis of the ICILS 2023 data, focusing on all European ICILS-2023-participants. The dataset includes 21 EU member states alongside five non-EU European countries, covering a total of 26 nations. Information on CIL was derived from standardized computer-based competency tests of 8th grade students conducted as part of ICILS 2023, which utilized interactive, scenario-based tasks to assess students' abilities in retrieving, evaluating, and creating digital information, ensuring objective and comparable assessment outcomes. Additional contextual data was gathered through student questionnaires (Fraillon, 2024). Altogether, the dataset encompasses data from 102.976 students across Europe, providing a robust and representative basis for examination. The analysis was performed using SPSS 30 software and the IEA IDB Analyser, ensuring adherence to international standards for large-scale educational assessments. The analysis proceeds in two steps to investigate the relationship between SES and CIL, as well as the influence of other contextual factors. In the first step, comparisons were made between students with low SES and their high SES counterparts within each participating country. For this purpose, mean CIL scores were calculated for both groups, and independent sample t-tests were performed to identify significant differences. In the second step, a hierarchical regression analysis was conducted to explore the cumulative influence of multiple predictors on CIL. The dependent variable across all models was CIL, while independent variables were introduced stepwise. In the first model, cultural capital, operationalized through the number of books in the household, served as the primary predictor. The second model included computer experience to assess its additional explanatory power. Gender was added as an independent variable in the third model to examine potential moderating effects. In the fourth model, migration background was introduced, followed by the inclusion of self-efficacy in the fifth model to evaluate its role in shaping CIL. This stepwise approach enabled a nuanced understanding of how these factors interact and contribute to explaining the variance in CIL outcomes (Raithel, 2006). This research ensures representativeness and generalizability by applying data weighting to compensate for sample biases arising from the cluster sampling design. To estimate sampling and measurement errors, the Jackknife Replication Method is utilized, enabling robust standard error calculations and reliable significance testing. For population-level competence estimates, the plausible value approach is employed, generating five plausible values per domain (Mislevy, 1991).

The analysis provides insights into the current situation of individual-level factors influencing CIL in European countries. The ICILS 2023 data reveal that European students achieve an average score of M = 477.46 in this domain, yet substantial variations exist depending on (a) SES, (b) migration background, (c) gender, (d) computer experience and (e) self-efficacy. A notable example is that 66.41% of participants are classified as having low SES, while 33.59% belong to the high SES category. On average, students from higher SES backgrounds achieve significantly higher CIL scores (M = 508.09) compared to their lower SES peers (M = 464.94). The findings indicate that students with low SES are significantly less likely to attain higher proficiency levels in CIL across all European countries. Regression analysis identified SES as the strongest predictor of CIL (b = 30.56), followed by computer experience (b = 16.41) and gender (b = 7.96), indicating that SES and prior digital exposure play a crucial role in shaping students' digital competencies. Negative effects of migration status on CIL were observed (b = -9.75), suggesting that structural barriers may limit access to digital resources and skill development, while higher ICT self-efficacy was associated with improved outcomes. The final model explained 17% of the variance in CIL scores in Europe. Overall, the findings indicate a strong connection between CIL and SES in Europe, emphasizing the significant contribution of family and student-level factors to understanding the digital divide. These findings are of considerable interest at the European level, as they illustrate the profound impact of SES and individual circumstances on students' CIL. By exposing disparities in digital competences among students from diverse backgrounds, they contribute to a deeper comprehension of CIL development processes. Furthermore, they provide a foundation for targeted interventions to bridge the digital divide across European countries.

Bachmann, R., Hertweck, F., Kamb, R., Lehner, J., & Niederstadt, M. (2022). Digitale Kompetenzen in Deutschland. ZfWP, 71(3), 266–286. ttps://doi.org/10.1515/zfwp-2022-2082 Bourdieu, P. (1973). Cultural Reproduction and Social Reproduction. In R. Brown (Ed.), Knowledge, Education, and Cultural Change (S. 71–84). Tavistock Publications. Eickelmann, B., Casamassima, G., Drossel, K., Fröhlich, N. (2024a). ICILS 2023 im Überblick: Zentrale Ergebnisse, Entwicklungen über ein Jahrzehnt und mögliche Entwicklungsperspektiven. Waxmann. Eickelmann, B., Fröhlich, N., Bos, W., Gerick, J., Goldhammer, F., Schaumburg, H., Schwippert, K., Senkbeil M., & J. Vahrenhold (Ed.). (2024b). ICILS 2023 # Deutschland: Computer- und informationsbezogene Kompetenzen und Kompetenzen im Bereich Computational Thinking von Schüler*innen im internationalen Vergleich. Waxmann. European Commission. (2020). Digital Education Action Plan 2021-2027: Resetting education and training for the digital age. Education and Training. Fraillon, J. (Ed.). (2024). An international perspective on digital literacy: Results from ICILS 2023. IEA. https://www.iea.nl/publications/icils-2023-international-report Frohn, J. (2020). Bildungsbenachteiligung im Ausnahmezustand. Ergebnisse einer Lehrkräftebefragung zur Verschärfung von Bildungsbenachteiligung im Lehren und Lernen auf Distanz. Zeitschrift für Schul- und Professionsentwicklung, 2(6), 59–83. Hatlevik, O.E., & Christophersen, K-A. (2013). Digital competence at the beginning of upper secondary school: Identifying factors explaining digital inclusion. Computers & Education, 63, 240–247. https://dx.doi.org/10.1016/j.compedu.2012.11.015 Hatlevik, O.E., Guðmundsdóttir, G. B., & Loi, M. (2015). Examining factors predicting students’ digital competence. Journal of Information Technology Education: Research, 14, 123–137. http://www.jite.org/documents/Vol14/JITEV14ResearchP123-137Hatlevik0873.pdf Hietajärvi, L., Mascheroni, G., Waechter, N., Järvinen, J., & Salmela-Aro, K. (2024). Latent profiles of adolescents’ digital skills across six European countries. new media & society, 1–23. Mislevy, R.J. (1991). Randomization-based inference about latent variables from complex samples. Psychometrika, 56(2), 177–196. https://doi.org/10.1007/BF02294457 OECD. (2001). “Understanding the Digital Divide”. OECD Digital Economy Papers, 49, OECD Publishing. http://dx.doi.org/10.1787/236405667766 Raithel, J. (2006). Quantitative Forschung.Ein Praxiskurs. VS Verlag für Sozialwissenschaften. Seaton, M., & Marsh, H.W., & Craven, R.G. (2010). Big-Fish-Little-Pond Effect: Generalizability and Moderation - Two Sides of the Same Coin. American Educational Research Journal, 47(2), 390–433. Trautwein, U., & Baeriswyl, F. (2007). Wenn leistungsstarke Klassenkameraden ein Nachteil sind. Referenzgruppeneffekte bei Übertrittsentscheidungen. Zeitschrift für Pädagogische Psychologie, 21(2), 119–133. Vodafone Foundation (Ed.) (2025). AI in European Schools: A European report comparing seven countries. Vodafone Foundation.