The influence of the home learning environment on middle school students’ use of ICT at school

Darren Pullen


The increasing use of information and communication technology (ICT) in schools has been largely explored in relation to student experience of coursework and school life. Students’ lives and experiences with technology beyond school have also begun to be explored. However, the nexus of the two domains is of yet an underdeveloped research area, yet anecdotally we know that technology use in either the home or school affects the other. This paper reports on a contemporary study-using structural equation modeling (SEM)- of students’ ICT use for social, leisure and school study, their attitudes towards that technology and their self-perceived competence with the technology across the domains of home and school. The study results suggest that technology usefulness and ease of use are key dimensions of students’ attitudes and acceptance towards ICT in both the home and school. Furthermore, the study found key pathway associations between different forms of technology used at home which determine the technology’s use at school and vice versa. Implications of these associations are that teachers and policy makers may use information about student technology use and technology competency to improve pedagogy and technology provision to bridge the gap between school and home.


Arbuckle, J. (2008). AMOS 17.0 user’s guide. SPSS, Chicago: IL.

Beckman, K., Bennett, S. & Lockyer, L. (2014a). Reconceptualising technology as a social tool: A secondary school student case study. In Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2014 (pp. 1554-1559). Chesapeake, VA: AACE.

Beckman, K., Bennett, S. & Lockyer, L. (2014b). Understanding students’ use and value of technology for learning. Learning, Media and Technology 39(3), 346-367.

Byrne, B. (2010). Structural equation modeling with AMOS - Basic concepts, applications, and programming. New York: Routledge.

Cheung, D., Hattie, J., & Ng, D. (2001). Re-examining the stages of concern questionnaire: A test of alternative models. The Journal of Educational Research, 9, 226-236.

Colbeck, D. (2007). Understanding knowledge genesis by means of multivariate factor analysis of epistemological belief structures. Information Research, 12(4). Retrieved from

Cole, D. & Pullen, D. (2010). Multiliteracies in motion. Routledge, New York.

Coorey, P. (2007, January). Rudd vows education revolution. The Sydney Morning Herald. Retrieved from

Dearing, R. (1997). Higher education in the learning society. London, United Kingdom: National Committee of Inquiry into Higher Education.

Department of Education, Training and Youth Affairs (DETYA). (2000). Learning for the knowledge society: An education and training action plan for the information economy. Retrieved from

Facer K. & Kent N. (2004) Different worlds? A comparison of young people's home and school ICT use. Journal of Computer Assisted Learning 20, 440–455.

Field, A. (2005). Discovering statistics using SPSS (2nd ed.). Thousand Oaks, CA: Sage Publications.

George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference, 11.0 update (4th ed.). Boston: Allyn and Bacon.

Hammond, M. (2013). Introducing ICT in schools in England: Rationale and consequences. BJET 45 (2), 191-201.

Harris, C., Straker, L. & Pollock, C. (2013). The influence of age, gender and other information technology use on young people’s computer use at school and home. Work: A journal of prevention, assessment and rehabilitation 44 (1), 61-71.

Hasebrink, U. (2014). Children's changing online experiences in a longitudinal perspective. Retrieved from

Hoyle, H. R. (1995). The structural equation modeling approach. In H. R. Hoyle (Ed.), Structural equation modeling: concepts, issues, and applications (pp. 1-15). Thousand Oaks, CA: Sage Publications.

J-F. , Swabey, K., Pullen, D. (2014). Pre-Service Teachers' Perception of Age Through a Developmental Lens, SAGE Open, 1-7. Retrieved from

Jodie, B. U. (2000). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn and Bacon.

Kaplan, D. (2000). Structural equation modeling: Foundations and extensions. Thousand Oaks, CA: Sage Publications.

Li, J., Snow, C. & White, C. (2014). Urban adolescent students and technology: access, use and interest in learning language and literacy. Innovations in learning language and teaching. Retrieved from

Malhotra, N., Hall, J., Shaw, M., & Crisp, M. (1996). Marketing research: An applied orientation. Sydney: Prentice Hall.

Nunnally, J. (1978). Psychometric theory. New York: McGraw-Hill.

Schreiber, J. B., Nora, A., Stage, F. K., Barlow, E. A., & King, J. (2006). Reporting structural equation modeling and confirmatory factor analysis results: A review. The Journal of Educational Research, 99, 323-337.

Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling (2nd ed.). London: Routledge.

Shipley, B. (2000). Cause and correlation in Biology: A user's guide to path analysis, structural equations and causal inference. Cambridge, UK: Cambridge University Press.

Statsoft. (2008). Structural equation modeling. Retrieved from

Ullman, J. B. (2001). Structural equation modeling. In B. G. Tabachnick & L. S. Fidell (Eds.), Using multivariate statistics (4th ed.). Needham Heights, MA: Allyn & Bacon.

United States of America, Department of Education. (2000). E-learning: Putting a world-class education at the fingertips of all children. The national educational technology plan. Retrieved from

Wastiau, P., Blamire, R., Kearney, C., Quittre, V., Van de Gaer, E. & Monseur, C. (2013). The use of ICT in education: a survey of schools in Europe. Education Journal of Education Research, Development & Policy 48 (1), 11-27.

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