Focusing on determinants of achievement progress in secondary education, many factors are known to foster or hinder achievement. Educational researchers, for instance, tend to agree that living in a supportive and culturally (cf. Bourdieu, 1986) or socially privileged environment (Coleman, 1988) is a contributing factor for achievement growth in education. Many scholars investigating the impact of individual factors on educational achievement conclude that migratory status (Petty, Harbaugh, & Wang, 2013), cultural capital and socio-economic status (Shin et al., 2013) and aptitude (Kyttäla & Björn, 2010) are relevant predictors for progress in reading, mathematics and science. Furthermore, research literature indicates that there are also compositional effects of classes and schools to take into account when education is under research (Hattie, 2002; Schofield, 2010; Thrupp, Lauder, & Robinson, 2002). Compared to this, only there is only a little body of research in the field of students’ computer literacy (cf. Fraillon, Schulz, & Ainley, 2013) and most literature is focusing of individual factors of students and teachers or on school characteristics, without referring to compositional measures (cf. Eickelmann, 2011; Gerick, Eickelmann, & Bos, accepted). The present paper works on this research gaps, using the data from the International Computer and Information Literacy Study (ICILS 2013).

From a theoretical perspective models conceptualizing students’ achievement and underlying factors on multiple layers of the educational system have been refined and make individual characteristics of students a main focus (e.g. Creemers, Kyriakides, & Sammons, 2010). Taking into account ongoing developments regarding ICT in schools and constant change towards an information society the desideratum of evaluating compositional effects with regards to students’ computer and information literacy (CIL) is worth to be addressed. Therefore the present paper focuses on the relevance of individual characteristics of students and their families, compositional effects on the school level and achievement in CIL in secondary education. In detail the following research questions are addressed with this contribution:

1. Is the achievement of computer and information literacy in secondary schools also affected by social- and achievement-related compositional effects?
2. And if so, which compositional effects (achievement related vs. social composition) are more relevant to students when individual characteristics are controlled for (gender, amount of books, SES, etc.)?
3. Do country-specific differences in the relevance of school compositional variables emerge between Denmark and Germany?

Bourdieu, P. (1986). The forms of capital. In J. G. Richardson (Ed.), Handbook of Theory and Research for the Sociology of Education (pp. 241-260). New York: Greenwood Press. Coleman, J.S. (1988). Social capital in the creation of human capital. American Journal of Sociology, 94, 95-120. Creemers, B.P.M., Kyriakides, L., & Sammons, P. (2010). Background to Educational Effectiveness Research. In B. P. M. Creemers, L. Kyriakides & P. Sammons (Eds.), Methodological Advances in Educational Effectiveness Research (pp. 3-18). Abingdon: Routledge. Eickelmann, B. (2011). Supportive and hindering factors to a sustainable implementation of ICT in schools. Journal for Educational Research Online, 3(1), 75-103. Fraillon, J., Ainley, J., Schulz, W., Friedman, T., & Gebhardt, E. (2014). Preparing for life in a digital age. The IEA International Computer and Information Literacy Study: Springer. Fraillon, J., Schulz, W., & Ainley, J. (2013). International Computer and Information Study: Assessment Framework. Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). Gerick, J., Eickelmann, B., & Bos, W. (accepted). School-level Predictors for ICT Use in Schools and Students’ Computer and Information Literacy. Large-scale Assessments in Education. Hattie, J. (2002). Classroom composition and peer effects. International Journal of Educational Research, 37(5), 449-481. Julian, M.W. (2001). The consequences of ignoring multilevel data structure in nonhierarchical covariance modeling. Structural Equation Modeling, 8(3), 325-352. Kyttäla, M., & Björn, P.M. (2010). Prior mathematics achievement, cognitive appraisals and anxiety as predictors of Finnish students' later mathematics performance and career orientation. Educational Psychology, 30(4), 431-448. Muthén, L.K., & Muthén, B.O. (2012). Mplus 7. Los Angeles, CA: Muthén & Muthén. Petty, T., Harbaugh, A.P., & Wang, C. (2013). Relationships between student, teacher, and school characteristics and mathematics achievement. School Science and Mathematics, 113(7), 333-344. Schofield, J.W. (2010). International evidence on ability grouping with curriculum differentiation and the achievement gap in secondary schools. Teachers College Record, 112(5), 1492-1528. Shin, T., Davison, M.L., Long, J.D., Chan, C.-K., & Heistad, D. (2013). Exploring gains in reading and mathematics achievement among regular and exceptional students using growth curve modeling. Learning and Individual Differences, 23(1), 92-100. Thrupp, M., Lauder, H., & Robinson, T. (2002). School composition and peer effects. International Journal of Educational Research, 37(5), 483-504.