Reading is a core competence for lifelong learning and participation in society. Thus, it is a crucial competence taught in schools. Nevertheless, substantial differences in reading achievements between different groups of students can be detected by the end of primary education (Mullis et al., 2017a). First, gender is an important individual predictor. Second, students’ cognitive skills are relevant with regard to their reading competence. Furthermore, especially in Germany and in many other Western countries socioeconomic status (SES) relates closely to reading competence. Another core grouping variable in this context shown by empirical studies is the students’ migration background.

However, digitalization has led to fundamental changes in the ways texts are presented and information is delivered (Mangen & van der Weel, 2016). Due to the use of ICT many new reading occasions appear to be present for young readers in addition to paper based texts. Especially during the ongoing COVID-19 pandemic using ICT in education has gained further importance and digital reading has an increased role, which reinforces the need to take a differentiated look at the relationship between digital reading and reading literacy. Research has shown that gender differences in digital reading still exist, but that they might be smaller than in paper-based assessments (Mullis et al., 2017b). Furthermore, it could be pointed out for the gender gap that affective variables such as reading motivation fully mediate the gender effect on reading digitally of primary school students (McElvany & Schwabe, 2019). Additionally, the familiarity with reading digital texts and the quality of instruction for using digital devices e.g. digital reading strategies might influence reading competence. The findings of a recent meta-analysis suggest that students’ ICT literacy differs between SES groups (Scherer & Siddiq, 2019). However, the relation between SES and ICT literacy was weaker than those reported in the reading domain. Despite it could be shown with a focus on SES, when learners do not have access to ICT resources, their overall reading achievement in both paper and online reading was lower (Leu et al., 2015). Research revealed hints that students' ICT attitudinal factors do have significant moderation effects on the relationship between their economic, social and cultural status and their reading literacy (Xiao & Hu, 2019). These findings for secondary school students even were discussed in light of the promotion of educational equality and the improvement of ICT-assisted reading education as ICT attitudinal factors might close the gap in reading literacy between students from high and low SES and different cultural backgrounds. Hence, it is important to investigate if already in primary schools the use of ICT might be beneficial for groups of students to foster reading literacy.

In light of the increasing importance of ICT in education and in society, the presentation focuses on the following questions:

• Is there a relation between the use of ICT and reading competence for different groups of students (taking into consideration gender, cognitive skills, SES and migration background) at the end of primary education?

• Which groups of students might especially benefit from using ICT in school with regard to reading literacy?  

• Can differences in the relation between the use of ICT and reading competence be explained by a certain aspect of instruction, that is the quality of teaching reading strategies for digital reading.

The analyses were based on the international published PIRLS 2016 data from fourth grade students in Germany (N = 3956; Mullis et al., 2017a). Missing values were treated by applying multiple imputation by chained equations using the R package mice (van Buuren & Groothuis-Oudshoorn, 2011) resulting in five imputed datasets. Sampling weights were used for all analyses to account for the hierarchical clustered sampling structure (Kolenikov, 2010). In order to answer the research questions various kinds of models were used, such as simple regression models as well as multilevel multi-group random slope models. These models were estimated with Mplus. For all analyses the internationally reported plausible values (Khorramdel, von Davier, Gonzalez & Yamamoto, 2020) were used as a measure of reading competence. Students’ indication of the frequency of using ICT for school-related purposes was analyzed. This was measured by one item with a four-level response scale reaching from “never or almost never” to “every day or almost every day”. Groups of students with regard to gender, cognitive skills, SES, and migration background were compared systematically. The quality of teaching reading strategies for digital reading was measured by four items, such as “Teach students strategies for reading digital texts” with a four-level response scale reaching from “never or almost never” to “every day or almost every day”. A scale reliability of 0.81 was achieved.

Results indicate an overall negative relation between the frequency of using ICT and individual reading competence. A more differentiated analysis showed that a moderate frequency of the use of ICT (once or twice a month) had no relation with reading competence while students who indicate a usage more often showed significantly lower reading competence on average (d > 0.3). However, these relationships showed a variance significantly different from zero when modelled within a random slope model indicating that these relationships vary across students. Further analyses showed that students with higher cognitive skills have significantly more positive relationships between the use of ICT and reading competence compared to students with lower cognitive abilities. No other predictors on student level could significantly explain variance of those relationships. Finally, the hypothesis that the quality of teaching reading strategies for digital reading on class level has a positive effect on the relationship between the use of ICT and reading competence was not supported by the data. This result also remained when a more differentiated analysis was conducted for the student subgroups mentioned above. The results are discussed critically with regard to implications for learning with ICT. Consideration is being given to how more groups might benefit from using ICT on reading literacy as a key competence for lifelong learning and participation. Especially under the COVID-19 pandemic restrictions on schooling and the increased digital reading considerations are important to support reading literacy of all groups of students.

Leu D. J., Forzani E., Rhoads C., Maykel C., Kennedy C., & Timbrell N. (2015). The new literacies of online research and comprehension: Rethinking the reading achievement gap. Reading Research Quarterly, 50(1), 37–59. Mangen, A., & van der Weel, A. (2016). The evolution of reading in the age of digitisation: An integrative framework for reading research. Literacy, 50(3), 116–124. McElvany, N., & Schwabe, F. (2019). Gender gap in reading digitally? Examining the role of motivation and self-concept. Journal for Educational Research Online, 11(1), 145–165. Khorramdel, L., von Davier, M., Gonzalez, E., & Yamamoto, K. (2020). Plausible Values: Principles of Item Response Theory and Multiple Imputations. In Large-Scale Cognitive Assessment (pp. 27-47). Springer, Cham. Kolenikov, S. (2010). Resampling variance estimation for complex survey data. The Stata Journal, 10(2), 165–199. Mullis, I. V. S., Martin, M. O., Foy, P., & Hooper, M. (2017a). PIRLS 2016 International Results in Reading. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/pirls2016/international-results/ Mullis, I. V. S., Martin, M. O., Foy, P., & Hooper, M. (2017b). ePIRLS 2016 International Results in Online Informational Reading. Retrieved from Boston College, TIMSS & PIRLS International Study Center website: http://timssandpirls.bc.edu/pirls2016/international-results/ Scherer, R., & Siddiq, F. (2019), The relation between students’ socioeconomic status and ICT literacy: Findings from a meta-analysis. Computers & Education, 138, 13–32. https://doi.org/10.1016/j.compedu.2019.04.011 van Buuren, S., & Groothuis-Oudshoorn, K. (2011). mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67. Xiao, Y., & Hu, J. (2019). The influence of ICT attitudes on closing the reading literacy gap of students from different economic, social and cultural backgrounds. 14th International Conference on Computer Science and Education (ICCSE), S. 60–64.