Abstract
Primary school children frequently use digital devices, which can be infected by computer viruses. In this mixed methods paper with two studies (N = 278 + 114), we examined 8-year-olds’ preconceptions about computer viruses and protection against them; how to teach these children about said topics using three different, 30-min-long, content-equivalent lessons; and what knowledge the children can acquire. We found that participants had limited prior knowledge of computer viruses and almost no knowledge about protection against them. However, they rarely had misconceptions. They learnt, and retained over a month, key general points and a few specific points about this domain. Acquired knowledge was still somewhat patchy, most likely represented in ‘pieces’ rather than as complex, theory-like chunks. Nevertheless, all three approaches produced notable learning gains (d > 1.78). A lesson organized around a narrative 5-min video and six < 1 min video snippets was the most effective: compared to a lesson organized around two 5-min videos (d = 0.89) and a teacher-led lesson without videos (d = 0.54). The findings are consistent with contemporary instructional design theories and ‘knowledge in pieces’ conceptual change frameworks. They imply that the topic of computer viruses should be included in second-graders’ curricula.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Notes
This age group has been chosen as the youngest possible cohort we believe can meaningfully acquire conceptual understanding of these topics in the context of the formal schooling system.
There is also a fourth paper about this topic: our own which is a preliminary analysis concerning prior knowledge from about 20% of the sample in the present Study 1 (Hannemann et al., 2019). The present article expands this preliminary analysis substantially. It also re-uses bits of text from our two previous papers ((i.e. Tsarava et al., 2020a; Hannemann et al., 2019); especially, as concerns the Methods and Background sections.
For the sake of scientific presentations, this video is available with English subtitles: https://decko.ceskatelevize.cz/video/b40500 © Czech Television. (Accessed 2022-Jul-01).
To estimate the robustness of our threshold for occurrence score, we also reran the analysis for different values (1–10%). This led to a slightly different number of themes retained for the analysis and also led to stricter statistical control for multiple comparisons. Nevertheless, we obtained similar results.
We also tried rounding up (i.e., counting code .5 as 1 for the sake of statistical analysis), which produced similar results. In fact, the use of code .5 in the thematic analysis was rare: For all themes except two, use of codes .5 was less than 3% for each theme. For the remaining two themes, it was 8% and 5%, respectively.
We also tested the differences between pre-/post-interview scores using tests of proportions and association using chi-square tests of independence delivering the same results.
In Study 1, this value was 0.35. Given the nature of computing Cohen’s d in linear mixed models, there can be small differences, as estimated standard deviations for random factors are dependent on the dataset used (which includes also the third group in Study 2).
https://www.imdb.com/title/tt5848272/ (Accessed 01-07-2022).
References
Ayres, P., & Sweller, J. (2014). The split-attention principle in multimedia learning. In The Cambridge handbook of multimedia learning (2nd ed., pp. 206–226).
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Brom, C., Lukavský, J., Greger, D., Hannemann, T., Straková, J., & Švaříček, R. (2020). Mandatory home education during the COVID-19 lockdown in the Czech Republic: a rapid survey of 1st-9th Graders' parents. Frontiers in Education, 5, Art. No. 103.
Brom, C., Stárková, T., & D’Mello, S. K. (2018). How effective is emotional design? A meta-analysis on facial anthropomorphisms and pleasant colors during multimedia learning. Educational Research Review, 25, 100–119.
Carretero, S., Vuorikari, R., & Punie, Y. (2017). DigComp 2.1: The digital competence framework for citizens with eight proficiency levels and examples of use. EUR 28558 EN, Joint Research Centre (Seville site).
CAS, C. A. S. (2012). Computer science: A curriculum for schools. Retrieved from https://www.computingatschool.org.uk/data/uploads/ComputingCurric.pdf
Chang, H. Y., Park, E.-J., Yoo, H.-J., won Lee, J., & Shin, Y. (2018). Electronic media exposure and use among toddlers. Psychiatry Investigation, 15(6), 568–573.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.
CSO, Czech statistical office (2020) Statistical yearbook of the Czech Republic 2020.
Diethelm, I., Hubwieser, P., & Klaus, R. (2012a). Students, teachers and phenomena: Educational reconstruction for computer science education. In Proceedings of the 12th Koli Calling international conference on computing education research (pp. 164–173). ACM.
Diethelm, I., Wilken, H., & Zumbrägel, S. (2012b). An investigation of secondary school students' conceptions on how the internet works. In Proceedings of the 12th Koli calling international conference on computing education research (pp. 67–73). ACM.
diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2–3), 105–225.
DiSessa, A. A. (2014). A history of conceptual change research: Threads and fault lines. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (2nd ed., pp. 88–108). Cambridge University Press.
Duit, R., Gropengiesser, H., Kattmann, U., Komorek, M., & Parchmann, I. (2012). The model of educational reconstruction—A framework for improving teaching and learning science. In Science education research and practice in Europe (pp. 13–37). Sense Publishers.
Fiorella, L., & Mayer, R. E. (2015). Learning as a generative activity. Cambridge University Press.
Gander, W., Petit, A., Berry, G., Demo, B., Vahrenhold, J., McGettrick, A., Boyle, R., Drechsler, M., Mendelson, A., Stephenson, C., & Ghezzi, C. (2013). Informatics education: Europe cannot afford to miss the boat. Report of the joint Informatics Europe & ACM Europe Working Group on Informatics Education.
Giesbrecht, F. G., & Burns, J. C. (1985). Two-stage analysis based on a mixed model: large-sample asymptotic theory and small-sample simulation results. Biometrics, 477–486.
Ginsburg, H. (1997). Entering the child's mind: The clinical interview in psychological research and practice. Cambridge University Press.
Glaser, M., Garsoffky, B., & Schwan, S. (2012). What do we learn from docutainment? Processing hybrid television documentaries. Learning and Instruction, 22(1), 37–46.
Guzdial, M. (2015). Learner-centered design of computing education: Research on computing for everyone. Morgan & Claypool Publishers.
Hannemann, T., Stárková, T., Ježek, P., Volná, K., Kačerovská, K., & Brom, C. (2019, October). Eight-year-olds’ conceptions of computer viruses: A quantitative study. In Proceedings of the 14th workshop in primary and secondary computing education (pp. 1–7).
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112.
Heintz, F., & Mannila, L. (2018). Computational thinking for all: an experience report on scaling up teaching computational thinking to all students in a major city in Sweden. In Proceedings of the 49th ACM technical symposium on computer science education (pp. 137–142). ACM.
Hubwieser, P., Giannakos, M.N., Berges, M., Brinda, T., Diethelm, I., Magenheim, J., Pal, Y., Jackova, J., & Jasute, E. (2015). A global snapshot of computer science education in K-12 schools. In Proceedings of the 2015 ITiCSE on working group reports (pp. 65–83). ACM.
Javora, O., Hannemann, T., Stárková, T., Volná, K., & Brom, C. (2019). Children like it more but don’t learn more: Effects of esthetic visual design in educational games. British Journal of Educational Technology, 50(4), 1942–1960.
Johnson, C. I., & Priest, H. A. (2014). The feedback principle in multimedia learning. In The Cambridge handbook of multimedia learning (pp. 449–463). Cambridge University Press.
Kafai, Y. B. (2008). Understanding virtual epidemics: Children’s folk conceptions of a computer virus. Journal of Science Education and Technology, 17(6), 523–529.
Kapon, S., & diSessa, A. A. (2012). Reasoning through instructional analogies. Cognition and Instruction, 30(3), 261–310.
Kardefelt-Winther, D. (2017). How does the time children spend using digital technology impact their mental well-being, social relationships and physical activity? An evidence-focused literature review, Innocenti Discussion Paper 2017-02. UNICEF.
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. (2017). lmerTest package: Tests in linear mixed effects models. Journal of Statistical Software, 82(1), 1–26.
Lister, R. (2016). Toward a developmental epistemology of computer programming. In Proceedings of the 11th workshop in primary and secondary computing education (pp. 5–16). ACM.
Mayer, R. E. (2021). Multimedia Learning (3rd ed.) (2nd ed.). Cambridge University Press.
Mayer, R. E. (Ed.) (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press.
Mayer, R. E., & Pilegard, C. (2014). Principles for managing essential processing in multimedia learning: Segmenting, pretraining, and modality principles. In The Cambridge handbook of multimedia learning (pp. 316–344). Cambridge University Press.
Minstrell, J. (1992). Facets of students’ knowledge and relevant instruction. Research in Physics Learning: Theoretical Issues and Empirical Studies, 110–128.
Moller, F., & Powell, S. (2019, October). Technoteach: Supporting computing teachers across Wales. In Proceedings of the 14th Workshop in Primary and Secondary Computing Education (pp. 1–2).
Özdemir, G., & Clark, D. B. (2007). An overview of conceptual change theories. Eurasia Journal of Mathematics, Science & Technology Education, 3(4), 351–361.
Plass, J. L., & Kalyuga, S. (2019). Four ways of considering emotion in cognitive load theory. Educational Psychology Review, 31(2), 339–359.
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/. (Accessed 25-Aug-2021)
Rey, G. D. (2012). A review of research and a meta-analysis of the seductive detail effect. Educational Research Review, 7(3), 216–237.
Šmahel, D., Machackova, H., Mascheroni, G., Dedkova, L., Staksrud, E., Ólafsson, K., Livingstone, S., & Hasebrink, U. (2020). EU Kids Online 2020: Survey results from 19 countries. London School of Economics and Political Science.
Sorva, J. (2012). Visual program simulation in introductory programming education. (Ph.D. thesis), Aalto University, Aalto University publication series.
Sundararajan, N., & Adesope, O. (2020). Keep it coherent: A meta-analysis of the seductive details effect. Educational Psychology Review, 32(3), 707–734.
Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive load theory. Springer.
Tsarava, K., Ninaus, M., Hannemann, T., Volná, K., Moeller, K., & Brom, C. (2020a, November). Fostering knowledge of computer viruses among children: The effects of a lesson with a cartoon series. In Proceedings of the 20th Koli calling international conference on computing education research (pp. 1–9).
Tsarava, K., Ninaus, M., Hannemann, T., Volná, K., Moeller, K., & Brom, C. (2020b). Fostering knowledge of computer viruses among children: The effects of a lesson with a cartoon series. In Koli Calling’20: Proceedings of the 20th Koli calling international conference on computing education research. Art. No. 10: ACM.
van Gog, T. (2014). The signaling (or cueing) principle in multimedia learning. In The Cambridge handbook of multimedia learning (pp. 263–278): Cambridge University Press.
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.
Acknowledgements
This study was conducted by inter-faculty Advanced Multimedia Learning Laboratory (AMuLab). It was primarily funded by the institutional funding from Charles University (Project PRIMUS/HUM/03 and Progress Q15). The study was also partially supported by a student grant project GA UK 684218. FD was supported by RVO 68081740. Data Newtown project was funded by Czech Television (www.ceskatelevize.cz), CZ.NIC (www.nic.cz) and Charles University (mff.cuni.cz); and we thank the whole team developing it. As concerns the research studies, we especially thank Tereza Fišerová for helping during the pilot study; and Karolina Faberová, Lucie Jičínská, Marek Kroufek, Kristýna Jószová, and Nikola Sochová for helping with data collection. Our thanks also include schools where we collected data: Prague primary schools Glowackého, J. Gutha-Jarkovského, K Dolům, Karla Čapka, Na Smetance, náměstí Curieových, Slovenská, Strossmayerovo náměstí, Šutka, U Obory, Veronské náměstí, and Vodičkova; and schools outside Prague: Hostivice, Třebízského (Kralupy nad Vltavou), and Poříčany.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Kateřina Kačerovská, Kristina Volná, Cyril Brom and Pavel Ježek are all co-authors of the Data Newtown series, so they declare a potential conflict of interest. Other authors declare no conflicts of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Brom, C., Hannemann, T., Tetourová, T. et al. Eight-year-olds’ naïve and acquired knowledge about computer viruses: a mixed methods study. Int J Technol Des Educ (2023). https://doi.org/10.1007/s10798-023-09847-5
Accepted:
Published:
DOI: https://doi.org/10.1007/s10798-023-09847-5