09 SES 03 B, Findings from ICILS 2013: Relating Individual, Class and School Characteristics to ICT Use and CIL Achievement
Many of the countries in the world are making efforts to improve their students’ achievement as well as their future competency by measuring the variety of competencies at the international level such as participating international studies of student assessment, for example PISA’s problem solving competency, IEA ICCS’s global citizenships and IEA ICILS’s Computer and Information Literacy.
ICILS 2013 is the first cross-national study to investigate the students’ acquisition of Computer and Information Literacy (CIL) as an essential skill in 21st century. The ICILS 2013 was implemented to measure CIL when students need to live in digital age and to gather information about the use of digital device and the related ICT capabilities, attitudes, as well as safety and security use. To measure CIL and the students several CIL related aspects and background information, the test modules and background questionnaires of students, teachers, principals, and ICT-coordinator were used at the study (Fraillon et al., 2013).
In order to clarify differences in educational attainment, several factors should be considered as an important impact at the different levels of education system. Most relevant factors can be the socio-economic status and self-efficacy of students, which is the basic information for students. In addition to these students’ variables, CIL score can be affected by the school contexts (e.g., school ICT resources, teachers’ ability using computer). Therefore, we need to investigate and understand the contexts related school level information, which effects on students’ CIL.
In the context of integrating new technologies in schools, factors contributing on either the teacher level or the school level are known to contribute to the use of ICT (Lorenz et al., 2015). Of course, the IT infrastructure in schools is an important factor regarding the use of ICT for learning purposes. Here, it seems to be well-accepted that ICT as such does not support learning itself but needs to be properly integrated into relevant teaching and learning strategies (Lai, 2008; Law et al., 2008). According to Eickelmann (2011) and ISTE (2009), there seems to be a range of supporting and hindering factors on the school level related to school leadership, attitudes and competencies of teachers as well as the ICT infrastructure in schools. As previous research reveals, that students’ ICT competency was affected by the level of ICT competency among the teachers and their willingness to use ICT in their lessons (Aoki et al., 2013, Law et al., 2008). These papers found that the ICT infrastructure of schools on its own is not enough to enhance the ICT competency but the efforts of the teachers and administration were more important than any other factor.
The purpose of the present study is to examine the relationship between the contextual variables and CIL scores. This study broadly examines several teacher effects to students’ achievement based on school ICT resource levels. In general, subgroups or subpopulations of schools may exist that differ substantially from the general population in ways that impact on the relationship between school level factors and student outcomes (Palardy, 2008). Therefore, this study is approached by analyzing the possible relationships of the context indicators using multiple group, multilevel Structural Equation Modeling (SEM) with CIL in an international comparative context. Here, it addresses the following research questions:
(1) Do the effects of student level variables on CIL achieving differ in low, middle, and high ICT resource schools?
(2) Do the effects of school level variables (especially teacher variables) on CIL achieving differ in low, middle, and high ICT resource schools?
References Aoki, H., Kim, J., & Lee, W. (2013). Propagation & level: Factors influencing in the ICT composite index at the school level. Computers & Education, 60(1), 310–324. doi:10.1016/j.compedu.2012.07.013 Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. In: K. A. Bollen & J. S. Long (Eds.), Testing structural equation models (pp. 136-162). Beverly Hills, CA: Sage. 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., Schulz, W. & Ainley, J. (2013). International Computer and Information Literacy Study: Assessment framework. Amsterdam: International Association for the Evaluation of Educational Achievement (IEA). Hu, L.T., & Bentler, P.M. (1999), "Cutoff Criteria for Fit Indexes in Covariance Structure Analysis: Conventional Criteria versus New Alternatives," Structural Equation Modeling, 6 (1), 1-55. ISTE (2009). Essential Conditions. Necessary conditions to effectively leverage technology for learning. Retrieved from http://www.iste.org/docs/pdfs/netsessentialconditions.pdf Lai, K.-W. (2008). IT and the learning process. In J. Voogt & G. Knezek (Eds.), International handbook of information technology in primary and secondary education (pp. 215–230). New York: Springer. Law, N., Pelgrum, W. J., & Plomp, T. (Eds.). (2008). Pedagogy and ICT use in schools around the world: Findings from the IEA SITES 2006 study. Hong Kong: CERC-Springer. Lorenz, R., Eickelmann, B. & Gerick, J. (2015). What Affects Students’ Computer and Information Literacy around the World? – An Analysis of School and Teacher Factors in High Performing Countries. In D. Slykhuis & G. Marks (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference 2015 (pp. 1212-1219). Chesapeake, VA: Association for the Advancement of Computing in Education (AACE). Muthén, B. O. (2004). Latent variable analysis. Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), The sage Handbook of quantitative methodology (S. 345-368). Thousand Oaks, CA: Sage. Muthén, L. K., & Muthén, B. O. (2012). Mplus user's guide. 7th. Los Angeles, CA: Muthén & Muthén. Palardy, G. J. (2008). Differential school effects among low, middle, and high social class composition schools: A multiple group, multilevel latent growth curve analysis. School Effectiveness and School Improvement, 19(1), 21-49.
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