Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Exploring models for increasing the effects of school information and communication technology use on learning outcomes through outside-school use and socioeconomic status mediation: the Ecological Techno-Process

  • 139 Accesses


Based on the ecological theories of educational technology, this study explored models for effective information and communication technology (ICT) use on learning outcomes, mediated by outside-school ICT use and socioeconomic status (SES), using structural equation modeling (SEM). Four models were developed based on empirical findings and validated using the 2012 Taiwanese sample of the Program for International Student Assessment to demonstrate model exploration. The four models measure the effects of ICT use on learning outcomes from (A) parallel ICT use, (B) inside-school ICT use with outside-school ICT use mediation, (C) Model A with SES mediation, and (D) Model B with SES mediation. Data analysis results indicate that the four models fit empirical data; Models C and D (with SES mediation) are superior to Models A and B based on fit indices; Models A and B are superior to Models C and D based on information criteria; and Models B–D (with mediation) provide more educational meaning than does Model A (without mediation). The results suggest new variables (i.e. outside-school ICT use and SES) and a modeling technique focusing on mediation effects (i.e. SEM) may be used to promote educational technology development by improving the effect of inside-school ICT use on traditional learning outcomes.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2


  1. Aesaert, K., van Braak, J., van Nijlen, D., & Vanderlinde, R. (2015). Primary school pupils’ ICT competences: Extensive model and scale development. Computers & Education,81, 326–344.

  2. Angeli, C., & Tsaggari, A. (2016). Examining the effects of learning in dyads with computer-based multimedia on third-grade students’ performance in history. Computers & Education,92, 171–180.

  3. Auld, G., & Johnson, N. F. (2015). Teaching the “other”: Curriculum “outcomes” and digital technology in the out-of-school lives of young people. In S. Bulfin, N. Johnson, & C. Bigum (Eds.), Critical perspectives on technology and education (pp. 163–181). New York: Palgrave Macmillan.

  4. Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. learning analytics (pp. 61–75). New York: Springer.

  5. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

  6. Barrera-Osorio, F., & Linden, L. L. (2009). The use and misuse of computers in education: Evidence from a randomized experiment in Colombia (Policy Research Working Paper 4836). Washington, D.C.: World Bank.

  7. Bollen, K. A., & Long, J. S. (1993). Testing structural equation models. Newbury Park: Sage.

  8. Bronfenbrenner, U. (1979). The ecology of human development: Experiments in nature and design. Cambridge: Harvard University Press.

  9. Bronfenbrenner, U. (1989). Ecological systems theory. In R. Vasta (Ed.), Annals of child development (Vol. 6, pp. 187–249). Greenwich: JAI Press.

  10. Bronfenbrenner, U., & Morris, P. A. (2006). The bioecological model of human development. In W. Damon & R. M. Lerner (Eds.), Handbook of child psychology, Vol. 1: Theoretical models of human development (6th ed., pp. 793–828). New York: Wiley.

  11. Chen, L. Y., Hsiao, B., Chern, C. C., & Chen, H. G. (2014). Affective mechanisms linking Internet use to learning performance in high school students: A moderated mediation study. Computers in Human Behavior,35, 431–443.

  12. Cheong, J., MacKinnon, D. P., & Khoo, S. T. (2003). Investigation of mediational processes using parallel process latent growth curve modeling. Structural Equation Modeling,10, 238–262.

  13. Cristia, J. P., Czerwonko, A., & Garofalo, P. (2010). Does ICT increase years of education? Evidence from Peru (IDB working paper OVE/WP-01/10). Washington, D.C.: Inter-American Development Bank.

  14. Enders, C. K., & Bandalos, D. L. (2001). The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Structural Equation Modeling,8, 430–457.

  15. Fariña, P., San Martín, E., Preiss, D. D., Claro, M., & Jara, I. (2015). Measuring the relation between computer use and reading literacy in the presence of endogeneity. Computers & Education,80, 176–186.

  16. Flumerfelt, S., & Green, G. (2013). Using lean in the flipped classroom for at risk Students. Educational Technology & Society,16(1), 356–366.

  17. Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. The Internet and Higher Education,10, 157–172.

  18. Hair, J. F., Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: A global perspective (7th ed.). Upper Saddle River: Pearson Education.

  19. Hamilton, E. R., Rosenberg, J. M., & Akcaoglu, M. (2016). The substitution augmentation modification redefinition (SAMR) model: A critical review and suggestions for its use. TechTrends,60, 433–441.

  20. Hammond, M. (2014). Introducing ICT in schools in England: Rationale and consequences. British Journal of Educational Technology,45, 191–201.

  21. Hohlfeld, T. N., Ritzhaupt, A. D., & Barron, A. E. (2010). Connecting schools, community, and family with ICT: Four-year trends related to school level and SES of public schools in Florida. Computers & Education,55, 391–405.

  22. Johnson, G. (2010a). Internet use and child development: The techno-microsystem. Australian Journal of Educational and Developmental Psychology,10, 32–43.

  23. Johnson, G. (2010b). Internet use and child development: Validation of the ecological techno-subsystem. Educational Technology and Society,13, 176–185.

  24. Johnson, G. M., & Puplampu, P. (2008). A conceptual framework for understanding the effect of the Internet on child development: The ecological techno-subsystem. Canadian Journal of Learning and Technology,34, 19–28.

  25. Johnston, K., Conneely, C., Murchan, D., & Tangney, B. (2015). Enacting key skills-based curricula in secondary education: Lessons from a technology-mediated, group-based learning initiative. Technology, Pedagogy and Education,24, 423–442.

  26. Junco, R., & Cotten, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education,59, 505–514.

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

  28. Kim, E. S., Yoon, M., Wen, Y., Luo, W., & Kwok, O. M. (2015). Within-level group factorial invariance with multilevel data: Multilevel factor mixture and multilevel MIMIC models. Structural Equation Modeling,22, 603–616.

  29. Kubiatko, M., & Vlckova, K. (2010). The relationship between ICT use and science knowledge for Czech students: A secondary analysis of PISA 2006. International Journal of Science and Mathematics Education,8, 523–543.

  30. Lee, C. D. (2016). Examining conceptions of how people learn over the decades through AERA presidential addresses: Diversity and equity as persistent conundrums. Educational Researcher,45(2), 73–82.

  31. Lee, Y. H., & Wu, J. Y. (2012). The effect of individual differences in the inner and outer states of ICT on engagement in online reading activities and PISA 2009 reading literacy: Exploring the relationship between the old and new reading literacy. Learning and Individual Differences,22, 336–342.

  32. Lemma, A. (2015). Psychoanalysis in times of technoculture: Some reflections on the fate of the body in virtual space. International Journal of Psychoanalysis,96, 569–582.

  33. Lewis, F., Butler, A., & Gilbert, L. (2011). A unified approach to model selection using the likelihood ratio test. Methods in Ecology and Evolution,2, 155–162.

  34. Lim, C.-P., Zhao, Y., Tondeur, J., Chai, C.-S., & Tsai, C.-C. (2013). Bridging the gap: Technology trends and use of technology in schools. Educational Technology & Society,16, 59–68.

  35. Livingstone, S., Carr, J., & Byrne, J. (2015). One in three: Internet governance and children’s rights. Ontario: Centre for International Governance Innovation; London: Royal Institute of International Affairs. https://www.cigionline.org/sites/default/files/no22_2.pdf.

  36. Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open and Distributed Learning,14, 202–227.

  37. Luu, K., & Freeman, J. G. (2011). An analysis of the relationship between information and communication technology (ICT) and scientific literacy in Canada and Australia. Computers & Education,56, 1072–1082.

  38. Malamud, O., & Pop-Eleches, C. (2011). Home computer use and the development of human capital. Quarterly Journal of Economics,126, 987–1027.

  39. Mama, M., & Hennessy, S. (2013). Developing a typology of teacher beliefs and practices concerning classroom use of ICT. Computers & Education,68, 380–387.

  40. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record,108, 1017–1054.

  41. Morgan, A. (2010). Interactive whiteboards, interactivity and play in the classroom with children aged three to seven years. European Early Childhood Education Research Journal,18, 93–104.

  42. Muir-Herzig, R. G. (2004). Technology and its impact in the classroom. Computers & Education,42, 111–131.

  43. Mumtaz, S. (2001). Children’s enjoyment and perception of computer use in the home and the school. Computers & Education,36, 347–362.

  44. Nasah, A., DaCosta, B., Kinsell, C., & Seok, S. (2010). The digital literacy debate: An investigation of digital propensity and information and communication technology. Educational Technology Research and Development,58, 531–555.

  45. Oberski, D. L. (2014). Lavaan.survey: An R package for complex survey analysis of structural equation models. Journal of Statistical Software,57(1), 1–27.

  46. Ocumpaugh, J., San Pedro, M. O., Lai, H. Y., Baker, R. S., & Borgen, F. (2016). Middle school engagement with mathematics software and later interest and self-efficacy for STEM careers. Journal of Science Education and Technology,25, 877–887.

  47. Organization for Economic Cooperation and Development. (2011). PISA 2009 Results: Students on line: Digital technologies and performance. Paris: Organization for Economic Cooperation and Development. https://doi.org/10.1787/9789264112995-en.

  48. Organization for Economic Cooperation and Development. (2013a). PISA 2012 assessment and analytical framework: Mathematics, reading, science, problem solving and financial literacy. Paris: OECD.

  49. Organization for Economic Cooperation and Development. (2013b). PISA 2012 results: Excellence through equity: Giving every student the chance to succeed (Vol. II). Paris: OECD.

  50. Organization for Economic Cooperation and Development. (2014). PISA 2012 technical report. Paris: OECD.

  51. Paiva, J. C., Morais, C., & Moreira, L. (2017). Activities with parents on the computer: An ecological framework. Journal of Educational Technology & Society,20(2), 1–14.

  52. Papastergiou, M. (2010). Enhancing physical education and sport science students’ self-efficacy and attitudes regarding information and communication technologies through a computer literacy course. Computers & Education,54, 298–308.

  53. Patarapichayatham, C., Kamata, A., & Kanjanawasee, S. (2012). Evaluation of model selection strategies for cross-level two-way differential item functioning analysis. Educational and Psychological Measurement,72, 44–51.

  54. Plesch, C., Kaendler, C., Rummel, N., Wiedmann, M., & Spada, H. (2013). Identifying Areas of Tension in the field of technology-enhanced learning: Results of an international Delphi study. Computers & Education,65, 92–105.

  55. Puentedura, R. (2014). Learning, technology, and the SAMR model: Goals, processes, and practice [Blog post]. Retrieved from http://www.hippasus.com/rrpweblog/archives/2014/06/29/LearningTechnologySAMRModel.pdf.

  56. Ravizza, S. M., Hambrick, D. Z., & Fenn, K. M. (2014). Non-academic internet use in the classroom is negatively related to classroom learning regardless of intellectual ability. Computers & Education,78, 109–114.

  57. Roehl, A., Reddy, S. L., & Shannon, G. J. (2013). The flipped classroom: An opportunity to engage millennial students through active learning strategies. Journal of Family & Consumer Sciences,105(2), 44–49.

  58. Rosseel, Y. (2012). lavaan: An R package for structural equation modeling. Journal of Statistical Software,48(2), 1–36.

  59. Rosseel, Y. (2015). The lavaan tutorial. Ghent: Department of Data Analysis, Ghent University.

  60. Samuelsson, U. (2010). ICT use among 13-year-old Swedish children. Learning, Media & Technology,35, 15–30.

  61. Sana, F., Weston, T., & Cepeda, N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Computers & Education,62, 24–31.

  62. Sánchez, J., & Salinas, A. (2008). ICT & learning in Chilean schools: Lessons learned. Computers & Education,51, 1621–1633.

  63. Schnoll, R. A., Fang, C. Y., & Manne, S. L. (2004). The application of SEM to behavioral research in oncology: Past accomplishments and future opportunities. Structural Equation Modeling,11, 583–614.

  64. Selwyn, N., Boraschi, D., & Özkula, S. M. (2009a). Drawing digital pictures: An investigation of primary pupils’ representations of ICT and schools. British Educational Research Journal,35, 909–928.

  65. Selwyn, N., & Gorard, S. (2003). Reality bytes: Examining the rhetoric of widening educational participation via ICT. British Journal of Educational Technology,34, 169–181.

  66. Selwyn, N., Potter, J., & Cranmer, S. (2009b). Primary pupils’ use of information and communication technologies at school and home. British Journal of Educational Technology,40, 919–932.

  67. Siddiq, F., Scherer, R., & Tondeur, J. (2016). Teachers’ emphasis on developing students’ digital information and communication skills (TEDDICS): A new construct in 21st century education. Computers & Education,92, 1–14.

  68. Tamim, R. M., Bernard, R. M., Borokhovski, E., Abrami, P. C., & Schmid, R. F. (2011). What forty years of research says about the impact of technology on learning a second-order meta-analysis and validation study. Review of Educational research,81, 4–28.

  69. Taylor, R. (1990). Interpretation of the correlation coefficient: A basic review. Journal of Diagnostic Medical Sonography,6, 35–39.

  70. Tempelaar, D. T., Rienties, B., & Giesbers, B. (2015). In search for the most informative data for feedback generation: Learning Analytics in a data-rich context. Computers in Human Behavior,47, 157–167.

  71. Thoemmes, F., MacKinnon, D. P., & Reiser, M. R. (2010). Power analysis for complex mediational designs using Monte Carlo methods. Structural Equation Modeling,17, 510–534.

  72. Tondeur, J., Sinnaeve, I., Van Houtte, M., & Van Braak, J. (2010). ICT as cultural capital: The relationship between socioeconomic status and the computer-use profile of young people. New Media & Society,13, 151–168.

  73. Tondeur, J., Van Braak, J., & Valcke, M. (2007). Curricula and the use of ICT in education: Two worlds apart? British Journal of Educational Technology,38, 962–976.

  74. Tucker-Drob, E. M., & Harden, K. P. (2012). Intellectual interest mediates gene × socioeconomic status interaction on adolescent academic achievement. Child Development,83, 743–757.

  75. Tudge, J. R., Mokrova, I., Hatfield, B. E., & Karnik, R. B. (2009). Uses and misuses of Bronfenbrenner’s bioecological theory of human development. Journal of Family Theory & Review,1, 198–210.

  76. Turkle, S. (2011). Alone together: Why we expect more from technology and less from each other. New York: Basic Books.

  77. Veira, A. K., Leacock, C. J., & Warrican, S. J. (2014). Learning outside the walls of the classroom: Engaging the digital natives. Australasian Journal of Educational Technology,30, 227–244.

  78. Vekiri, I. (2010). Socioeconomic differences in elementary students’ ICT beliefs and out-of-school experiences. Computers & Education,54, 941–950.

  79. Wang, S. K., Hsu, H. Y., Campbell, T., Coster, D. C., & Longhurst, M. (2014). An investigation of middle school science teachers and students use of technology inside and outside of classrooms: Considering whether digital natives are more technology savvy than their teachers. Educational Technology Research and Development,62, 637–662.

  80. Wellington, J. (2001). Exploring the secret garden: The growing importance of ICT in the home. British Journal of Educational Technology,32, 233–244.

  81. Wu, L., Lu, W., & Li, Y. (2016). Effects of video games and online chat on mathematics performance in high school: An approach of multivariate data analysis. International Journal of Social, Behavioral, Educational, Economic, Business and Industrial Engineering,10, 1286–1289.

  82. Wurst, C., Smarkola, C., & Gaffney, M. A. (2008). Ubiquitous laptop usage in higher education: Effects on student achievement, student satisfaction, and constructivist measures in honors and traditional classrooms. Computers & Education,51, 1766–1783.

  83. Zeidler, D. L. (2016). STEM education: A deficit framework for the twenty first century? A sociocultural socioscientific response. Cultural Studies of Science Education,11(1), 11–26.

  84. Zhou, M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computers & Education,92, 194–203.

Download references


The author wishes to express her gratitude to the editors for thoughtful suggestions and comments on the article. This study was funded by the Ministry of Science and Technology, Taiwan (MOST 103-2410-H-004-137; MOST 104-2410-H-004-143-MY2; MOST 106-2410-H-004-131; MOST 108-2511-H-004-002). The funder only provides financial support and does not substantially influence the entire research process, from study design to submission. The authors are fully responsible for the content of the paper. There are no potential conflicts of interests with respect to the authorship and/or publication of this article.


This study was funded by the Ministry of Science and Technology, Taiwan (MOST 103-2410-H-004-137; MOST 104-2410-H-004-143-MY2; MOST 106-2410-H-004-131; MOST 108-2511-H-004-002).

Author information

Correspondence to Mei-Shiu Chiu.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest

Ethical approval

This study used open data freely available to the public. The data source and sample were fully acknowledged in the paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Chiu, M. Exploring models for increasing the effects of school information and communication technology use on learning outcomes through outside-school use and socioeconomic status mediation: the Ecological Techno-Process. Education Tech Research Dev 68, 413–436 (2020). https://doi.org/10.1007/s11423-019-09707-x

Download citation


  • Achievement
  • Ecological theories
  • Evaluation methodology
  • ICT use
  • Structural equation modeling