09 SES 11 A, Findings From International Comparative Achievement Studies (Part 2): Investigating Construct Dimensions and Relations in Comparative and Contrastive Perspectives
Symposium continued from 09 SES 10 A
Information and communication technology (ICT) skills have become increasingly relevant over the last decades (Fraillon et al., 2014). Ongoing transitions towards an information or knowledge society emphasize the necessity to build on new competences for young people in order for them to participate effectively in the digital age (ibid; Voogt & Knezek, 2008). The IEA’s International Computer and Information Literacy Study (ICILS 2013) is the first large scale assessment which succeeded in measuring such students’ computer and information literacy (CIL) on a level of international comparison (Fraillon et al., 2014). Theoretical considerations as well as empirical findings lead to the assumption that CIL might be related to students’ reading literacy (Fraillon et al., 2013, Fraillon et al., 2014). However, an empirical examination of this relationship between the two constructs is still pending and constitutes the core objective of the presented research. Relying on the IEA-ICILS 2013 representative data base and using a national extension of a reading test for Germany’s student sample, this presentation explores the relationship of the afore-mentioned competence domains by means of path analysis (Byrne, 2012). In-depth analyses further include students’ background variables. Results indicate that CIL merely represents an independent construct. Modeling the direct effect of reading literacy on CIL the path analyses reveal a middle size effect of .443 and the reading construct accounts for a mere 20 percent of the variance in the CIL construct. Additionally, a correlative relationship between the two constructs is tested and it turns out that the model with the direct effect of reading on CIL fits the data best. Moreover, the students’ socioeconomic background has an effect on reading literacy as well as on CIL in the model, while the students’ gender only has an effect on reading achievement and students’ migration background has no effect on either.
Byrne, B. (2012). Structural Equation Modeling with Mplus. New York: Routledge Press. Fraillon, J., Schulz, W. & Ainley, J. (2013). Assessment Framework. The Netherlands: International Association for the Evaluation of Educational Achievement (IEA). Fraillon, J., Ainley, J., Schulz, W., Friedman, T. & Gerbhardt, E. (2014). Preparing for Life in a Digital Age. The IEA International Computer and Literacy Information Study International Report. The Netherlands: International Association for the Evaluation of Educational Achievement (IEA). Voogt, J. M., & Knezek, G. (Eds.). (2008). International Handbook of Information Technology in Primary and Secondary Education. New York: Springer.
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