Are Schools Alienating Digitally Engaged Students? Longitudinal Relations between Digital Engagement and School Engagement

Main Article Content

Lauri Hietajärvi
Kirsti Lonka
Kai Hakkarainen
Kimmo Alho
Katariina Salmela-Aro


This article examined digital learning engagement as the out-of-school learning component that reflects informally emerging socio-digital participation. The gap hypothesis proposes that students who prefer learning with digital technologies outside of school are less engaged in traditional school. This hypothesis was approached from the framework of connected learning, referring to the process of connecting self-regulated and interest-driven learning across formal and informal contexts. We tested this hypothesis with longitudinal data. It was of interest how digital engagement, operationalized as a general digital learning preference, wish for digital schoolwork, and their interaction, is related to traditional school engagement. This was examined both cross-sectionally in three time points and longitudinally across three years. The participants were 1,705 (43.7% female) 7th–9th graders (13-15 years old) from 27 schools in Helsinki, Finland. We explored the structure of correlations between latent constructs at each time point separately, and finally, to evaluate longitudinal relations between digital engagement and school engagement we specified latent cross-lagged panel models. The results indicate that students holding a stronger general digital learning preference experienced higher schoolwork engagement, both contemporaneously and over time, indicating successful connected learning. However, the results also showed support for the gap hypothesis: Students who preferred digital learning but did not have the chance to digitally engage at school, experienced a decrease in school engagement over time. The article shows that there is a need to examine the reciprocal interactive processes between the learners and their social ecologies inside and outside school more closely.      

Article Details

How to Cite
Hietajärvi, L., Lonka, K., Hakkarainen, K., Alho, K., & Salmela-Aro, K. (2020). Are Schools Alienating Digitally Engaged Students? Longitudinal Relations between Digital Engagement and School Engagement. Frontline Learning Research, 8(1), 33–55.


Asparouhov, T., & Muthen, B. (2006). Comparison of estimation methods for complex survey data analysis. Mplus Web Notes. URL:

Barron, B. (2006). Interest and self-sustained learning as catalysts of development: A learning ecology perspective. Human Development, 49, 193–224. doi: 10.1159/000094368

Barron, B., Martin, C. K., Takeuchi, L., & Fithian, R. (2009). Parents as learning partners in the development of technological fluency. International Journal of Learning and Media, 1, 55–77. doi: 10.1162/ijlm.2009.0021

Bennett, D. A. (2001). How can I deal with missing data in my study? Australian and New Zealand Journal of Public Health, 25, 464–469. doi: 10.1111/j.1467-842X.2001.tb00294.x

Bennett, S. & Maton, K. (2010). Beyond the “digital native” debate: Towards a more nuanced understanding of students’ technology experiences. Journal of Computer Assisted Learning, 26, 321–331. doi: 10.1111/j.1365-2729.2010.00360.x

Bereiter, C. & Scardamalia, M. (1993). Surpassing Ourselves: an Inquiry Into the Nature and Implications of Expertise. Chicago, IL: Open Court.

Boekaerts, M., & Minnaert, A. (1999). Self-regulation with respect to informal learning. International Journal of Educational Research, 31, 533–544. doi: 10.1016/S0883-0355(99)00020-8

Chen, F. F. (2007). Sensitivity of goodness of fit indices to lack of measurement invariance. Structural Equation Modeling, 14, 464–504. doi: 10.1080/10705510701301834

Chen, P. S. D., Lambert, A. D., & Guidry, K. R. (2010). Engaging online learners: The impact of Web-based learning technology on college student engagement. Computers & Education, 54, 1222–1232. doi: 10.1016/j.compedu.2009.11.008

Clements, J. C. (2015). Using Facebook to enhance independent student engagement: A case study of first-year undergraduates. Higher Education Studies, 5, 131–146. doi: 10.5539/hes.v5n4p131

Demerouti, E., Bakker, A. B., Nachreiner, F., & Schaufeli, W. B. (2001). The job demands-resources model of burnout. Journal of Applied Psychology, 86, 499–512. doi: 10.1037/0021-9010.86.3.499

Deng, L., Connelly, J., & Lau, M. (2016). Interest-driven digital practices of secondary students: Cases of connected learning. Learning, Culture and Social Interaction, 9, 45–54. doi: 10.1016/j.lcsi.2016.01.004

Epskamp, S., Cramer, A. O., Waldorp, L. J., Schmittmann, V. D., & Borsboom, D. (2012). qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–18. doi:

Epskamp, S., & Fried, E. I. (2018). A Tutorial on Regularized Partial Correlation Networks. Psychological Methods, 23(4), 617-634.. doi: 10.1037/met0000167

Epskamp, S., Rhemtulla, M., & Borsboom, D. (2017). Generalized network psychometrics: Combining network and latent variable models. Psychometrika, 82, 904–927. doi: 10.1007/s11336-017-9557-x

Esteves, K. K. (2012). Exploring Facebook to Enhance Learning and Student Engagement: A Case from the University of Philippines (UP) Open University. Malaysian journal of distance education, 14.

EU Kids Online (2014) EU Kids Online: Findings, methods, recommendations (deliverable D1.6). EU Kids Online, London, United Kingdom: London School of Economics.

European Commission. (2017). The Digital Competence Framework 2.0. Retrieved from

European Parliament. (2015). Innovative schools: Teaching and learning in the digital era - workshop documentation. Brussels, Belgium: European Parliament.

Fredricks, J.A., Blumenfeld, P.C., & Paris, A.H. (2004). School engagement: Potential of the concept, state of the evidence. Review of Educational Research, 74, 59–109. doi: 10.3102/00346543074001059

Fruchterman. T.M.J. & Reingold, E.M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21, 1129–1164. doi: 10.1002/spe.4380211102

Gurung, B., & Rutledge, D. (2014). Digital learners and the overlapping of their personal and educational digital engagement. Computers & Education, 77, 91–100. doi: 10.1016/j.compedu.2014.04.012

Guyon, H., Falissard, B., & Kop, J.-L. (2017). Modeling psychological attributes in discussion: Network analysis vs. latent variables. Frontiers in Psychology, 8, 798. doi: 10.3389/fpsyg.2017.00798

Hakkarainen, K. (2009). A knowledge-practice perspective on technology-mediated learning. International Journal of Computer-Supported Collaborative Learning, 4, 213–231. doi: 10.1007/s11412-009-9064-x

Hakkarainen, K., Ilomäki, L., Lipponen, L., Muukkonen, H., Rahikainen, M., Tuominen, T., et al. (2000). Students’ skills and practices of using ICT: Results of a national assessment in Finland. Computers & Education, 34, 103–117. doi:10.1016/S0360-1315(00)00007-5

Hallquist, M. N. & Wiley, J. F. (2018). MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus. Structural Equation Modeling, 1–18. doi: 10.1080/10705511.2017.1402334.

Halonen, N., Hietajärvi, L., Lonka, K., & Salmela-Aro, K. (2016). Sixth graders’ use of technologies in learning, technology attitudes and school well-being. The European Journal of Social & Behavioural Sciences. 18, 2307–2324. doi: 10.15405/ejsbs.205

Hamaker, E. L., Kuiper, R. M., & Grasman, R. P. (2015). A critique of the cross-lagged panel model. Psychological Methods, 20, 102. doi: 10.1037/a0038889

Hatano, G. & Inagaki, K. (1992). Desituating cognition through the construction of conceptual knowledge. In P. Light & G. Butterworth (Eds.), Context and Cognition. Ways of Knowing and Learning (pp. 115–133). New York, New York: Harvester.

Hietajärvi, L., Salmela-Aro, K., Tuominen, H., Hakkarainen, K., & Lonka, K. (2019). Beyond screen time: Multidimensionality of socio-digital participation and relations to academic well-being in three educational phases. Computers in Human Behavior, 93, 13–24. doi: 10.1016/j.chb.2018.11.049

Howard, S. K., Ma, J., & Yang, J. (2016). Student rules: Exploring patterns of students’ computer-efficacy and engagement with digital technologies in learning. Computers & Education, 101, 29–42. doi: 10.1016/j.compedu.2016.05.008

Hu, L. T., & Bentler, P. M. (1998). Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification. Psychological Methods, 3, 424.

Ito, M., Baumer, S., Bittanti, M., Cody, R., Herr-Stephenson, B., Horst, H. A., et al. (2010). Hanging Out, Messing Around, and Geeking Out. Cambridge, Massachusetts: The MIT Press.

Ito, M., Gutiérrez, K., Livingstone, S., Penuel, B., Rhodes, J., Salen, K., ... & Watkins, S. C. (2013). Connected Learning: An Agenda for Research and Design. Irvine, California: Digital Media and Learning Research Hub.

Jenkins, H. (2009). Confronting the Challenges of Participatory Culture: Media Education for the 21st Century. Cambridge, Massachusetts: MIT Press.

Junco, R. (2012a). The relationship between frequency of Facebook use, participation in Facebook activities, and student engagement. Computers & Education, 58, 162–171. doi: 10.1016/j.compedu.2011.08.004

Junco, R. (2012b). Too much face and not enough books: The relationship between multiple indices of Facebook use and academic performance. Computers in Human Behavior, 28, 187–198. doi: 10.1016/j.chb.2011.08.026

Junco, R., Heiberger, G., & Loken, E. (2011). The effect of Twitter on college student engagement and grades. Journal of Computer Assisted Learning, 27, 119–132. doi: 10.1111/j.1365-2729.2010.00387.x

Klein, A., & Moosbrugger, H. (2000). Maximum likelihood estimation of latent interaction effects with the LMS method. Psychometrika, 65, 457–474. doi: 10.1007/BF02296338

Kumpulainen, K., & Sefton-Green, J. (2012). What is connected learning and how to research it? International Journal of Learning, 4, 7–18. doi: 10.1162/IJLM_a_00091

Laird, T. F. N., & Kuh, G. D. (2005). Student experiences with information technology and their relationship to other aspects of student engagement. Research in Higher Education, 46, 211–233. doi: 10.1007/s11162-004-1600-y

Li, S., Hietajärvi, L., Palonen, T., Salmela-Aro, K., & Hakkarainen, K. (2017). Adolescents’ social networks: Exploring different patterns of socio-digital participation. Scandinavian Journal of Educational Research, 61, 255–274. doi: 10.1080/00313831.2015.1120236

Little, T. D., Preacher, K. J., Selig, J. P., & Card, N. A. (2007). New developments in latent variable panel analyses of longitudinal data. International Journal of Behavioral Development, 31, 357–365. doi: 10.1177/0165025407077757

MacCallum, R.C., Browne, M.W., & Cai, L. (2005). Testing differences between nested covariance structure models: Power analysis and null hypotheses. Psychological Methods, 11, 19–35. doi: 10.1037/1082-989X.11.1.19

Malcolm, J., Hodkinson, P., & Colley, H. (2003). The interrelationships between informal and formal learning. Journal of Workplace Learning, 15, 313–318. doi: 10.1108/13665620310504783

Marsh, H. W., Wen, Z., & Hau, K. T. (2004). Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction. Psychological Methods, 9, 275. doi: 10.1037/1082-989X.9.3.275

McFarlane, A. (2015). Authentic Learning for the Digital Generation: Realising the Potential of Technology in the Classroom. London: Routledge.

Moisala, M., Salmela, V., Hietajärvi, L., Carlson, S., Vuontela, V., Lonka, K., ... & Alho, K. (2016a). Gaming is related to enhanced working memory performance and task-related cortical activity. Brain Research, 1655, 204–215. doi: 10.1016/j.brainres.2016.10.027

Moisala, M., Salmela, V., Hietajärvi, L., Salo, E., Carlson, S., Salonen, O., ... & Alho, K. (2016b). Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. NeuroImage, 134, 113–121. doi: 10.1016/j.neuroimage.2016.04.011

Muthén, L. K., & Muthén, B. O. (2018). Mplus: Statistical Analysis with Latent Variables: User's Guide [version 8]. Los Angeles, California: Muthén & Muthén.

Muthén, B., & Satorra, A. (1995). Complex sample data in structural equation modeling. Sociological Methodology, 25, 267–316. doi: 10.2307/271070

Nardi, B., & O’Day, V. (2000). Information Ecologies: Using Technology with Heart. Cambridge, Massachussets: MIT.

OECD. (2015). Students, Computers and Learning: Making the Connection. Paris, France: PISA, OECD Publishing. doi: 10.1787/9789264239555-en

Olson, D.R. & Bruner, J. S. (1996) Folk Psychology and Folk Pedagogy. In D. R. Olson, D.R. & N. Torrance (Eds.) The Handbook of Education and Human Development. New Models of Learning, Teaching and Schooling (pp. 9–27). Malden, Massachusetts: Blackwell Publisher.

Orvis, K. L. (Ed.). (2008). Computer-Supported Collaborative Learning: Best Practices and Principles for Instructors: Best Practices and Principles for Instructors. Hershey, New York, New York: IGI Global.

Paavola S. & Hakkarainen, K. (2014). Trialogical approach for knowledge creation. In Tan S-C., Jo, H.-J., & Yoe, J. (Eds.), Knowledge Creation in Education (pp. 53–72). Singapore: Springer.

Panadero, E. & Järvelä, S. (2015). Socially shared regulation of learning: A review. European Psychologist, 20, 190–203. doi: 10.1027/1016-9040/a000226

Prensky, M. (2001). Digital natives, digital immigrants part 1. On the Horizon, 9, 1–6. doi: 10.1108/10748120110424816

Putnick, D. L., & Bornstein, M. H. (2016). Measurement invariance conventions and reporting: the state of the art and future directions for psychological research. Developmental Review, 41, 71–9. doi: 10.1016/j.dr.2016.06.004

R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing,

Vienna, Austria. URL

Raftery, A. E. (1995). Bayesian model selection in social research. Sociological Methodology, 111-163.

Rajala, A., Kumpulainen, K., Hilppö, J., Paananen, M., & Lipponen, L. (2015). Connecting learning across school and out-of-school contexts: A review of pedagogical approaches. In O. Erstad, K. Kumpulainen, Å. Mäkitalo, K. P. Pruulmann-Vengerfeldt, & T. Jóhannsdóttir (Eds.), Learning Across Contexts in the Knowledge Society. (pp. 15-35) Rotterdam, The Netherlands: Sense Publishers.

Rashid, T., & Asghar, H. M. (2016). Technology use, self-directed learning, student engagement and academic performance: Examining the interrelations. Computers in Human Behavior, 63, 604–612. doi: 10.1016/j.chb.2016.05.084

Ritella, G. & Hakkarainen, K (2012). Instrument genesis in technology mediated learning: From double stimulation to expansive knowledge practices. International Journal of Computer-Supported Collaborative Learning, 7, 239–258 doi: 10.1007/s11412-012-9144-1.

Robinson, K. (2011). Out of our Minds. Learning to be Creative. Westford, Massachusetts: Capstone Publishing Inc.

Salmela-Aro, K. (2017). Dark and bright sides of thriving–school burnout and engagement in the Finnish context. European Journal of Developmental Psychology, 14, 337–349. doi: 10.1080/17405629.2016.1207517

Salmela-Aro, K., Muotka, J., Alho, K., Hakkarainen, K., & Lonka, K. (2016a). School burnout and engagement profiles among digital natives in Finland: A person-oriented approach. European Journal of Developmental Psychology, 13, 704–718. doi: 10.1080/17405629.2015.1107542

Salmela-Aro, K., & Upadaya, K. (2012). The schoolwork engagement inventory. European Journal of Psychological Assessment, 28, 60–67. doi: 10.1027/1015-5759/a000091

Salmela-Aro, K., & Upadyaya, K. (2014). School burnout and engagement in the context of demands–resources model. British Journal of Educational Psychology, 84, 137–151. doi: 10.1111/bjep.12018

Salmela-Aro, K., Upadyaya, K., Hakkarainen, K., Lonka, K., & Alho, K. (2016b). The dark side of internet use: Two longitudinal studies of excessive internet use, depressive symptoms, school burnout and engagement among Finnish early and late adolescents. Journal of Youth and Adolescence, 1–15. doi: 10.1007/s10964-016-0494-2

Sawyer, K. (2014). The Cambridge Handbook of the Learning Sciences. Cambridge, Massachusetts: Cambridge University Press.

Selig, J. P., & Little, T. D. (2012). Autoregressive and cross-lagged panel analysis for longitudinal data. In B. Laursen, T. D. Little, & N. Card (Eds.). Handbook of Developmental Research Methods (pp. 265–278). New York, New York: Guilford Press.

Selwyn, N. (2006). Exploring the ‘digital disconnect’ between net savvy students and their schools. Learning, Media and Technology, 31, 5–17. doi: 10.1080/17439880500515416

Sung, Y. T., Chang, K. E., & Liu, T. C. (2016). The effects of integrating mobile devices with teaching and learning on students’ learning performance: A meta-analysis and research synthesis. Computers & Education, 94, 252–275. doi: 10.1016/j.compedu.2015.11.008

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. doi: 10.3102/0034654310393361

Upadyaya, K., & Salmela-Aro, K. (2013). Development of school engagement in association with academic success and well-being in varying social contexts: A review of empirical research. European Psychologist, 18, 136–147. doi: 10.1027/1016-9040/a000143

Wexler, B. E. (2006). Brain and culture. Neurobiology, ideology, and social change. Cambridge, Massachusetts: The MIT Press.