New Materialist Network Approaches in Science Education: A Method to Construct Network Data from Video

Main Article Content

Miikka Turkkila
Jari Lavonen
Katariina Salmela-Aro
Kalle Juuti

Abstract

Lately, new materialism has been proposed as a theoretical framework to better understand material-dialogic relationships in learning, and concurrently network analysis has emerged as a method in science education research. This paper explores  how to include materiality in network analysis  and reports the development of a method to construct network data from video. The approaches, 1) information flow, 2) material semantic and 3) material engagement, were identified based on the literature on network analysis and new materialism in science education. The method was applied and further improved with a video segment from an upper secondary school physics lesson.  The example networks from the video segment show that network analysis is a potential research method within the materialist framework and that the method allows studies into the material and dialogic relationships that emerge when students are engaged in investigations in school.

Article Details

How to Cite
Turkkila, M., Lavonen, J., Salmela-Aro, K., & Juuti, K. (2022). New Materialist Network Approaches in Science Education: A Method to Construct Network Data from Video. Frontline Learning Research, 10(2), 44. https://doi.org/10.14786/flr.v10i2.949 (Original work published October 14, 2022)
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References

Ash, D. (2007). Using video data to capture discontinuous science meaning making in non-school settings. In Video Research in the Learning Sciences (pp. 221–240). Routledge.

https://doi.org/10.4324/9780203877258

Barabási, A.-L. (2011). The network takeover. Nature Physics, 8 (1), 14–16.

https://doi.org/10.1038/nphys2188

Barrat, A., Barthélemy, M., Pastor-Satorras, R., & Vespignani, A. (2004). The architecture of complex weighted networks. Proceedings of the National Academy of Sciences of the United States of America, 101(11), 3747–3752. https://doi.org/10.1073/pnas.0400087101

Bennett, J. (2010). Vibrant Matter: A Political Ecology of Things. Durham: Duke University Press. https://doi.org/10.2307/j.ctv111jh6w

Bokhove, C. (2018). Exploring classroom interaction with dynamic social network analysis. International Journal of Research and Method in Education, 41(1), 17–37.

https://doi.org/10.1080/1743727X.2016.1192116

Borgatti, S. P. (2005). Centrality and network flow. Social Networks, 27 (1), 55–71.

https://doi.org/10.1016/j.socnet.2004.11.008

Bressler, D. M., Bodzin, A. M., Eagan, B., & Tabatabai, S. (2019). Using epistemic network analysis to

examine discourse and scientific practice during a collaborative game. Journal of Science

Education and Technology, 28(5), 553–566. https://doi.org/10.1007/s10956-019-09786-8

Bruun, J., Lindahl, M., & Linder, C. (2019). Network analysis and qualitative discourse analysis of a classroom group discussion. International Journal of Research and Method in Education, 42(3), 317–339. https://doi.org/10.1080/1743727X.2018.1496414

Caballero, D., Pikkarainen, T., Araya, R., Viiri, J., & ... (2020). Conceptual network of teachers’ talk: Automatic analysis and quantitative measures. FMSERA Journal, 3(1), 18–31. Retrieved from https://journal.fi/fmsera/article/view/79630

Cook, V., Warwick, P., Vrikki, M., Major, L., & Wegerif, R. (2019). Developing material-dialogic space in geography learning and teaching: Combining a dialogic pedagogy with the use of a microblogging tool. Thinking Skills and Creativity, 31, 217-231. https://doi.org/10.1016/j.tsc.2018.12.005

Derry, S. J., Pea, R. D., Barron, B., Engle, R. A., Erickson, F., Goldman, R., Hall, R., Koschmann, T., Lemke, J. L., Sherin, M. G., & Sherin, B. L. (2010). Conducting video research in the learning sciences: guidance on selection, analysis, technology, and ethics. Journal of the Learning Sciences, 19 (1), 3–53. https://doi.org/10.1080/10508400903452884

Dou, R., & Zwolak, J. P. (2019). Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting. Physical Review Physics Education Research, 15 (2), 20105. https://doi.org/10.1103/PhysRevPhysEducRes.15.020105

ELAN (Version 5.4) [Computer software.] (2019). Nijmegen: Max Planck Institute for Psycholinguistics, the Language Archive. Retrieved from https://archive.mpi.nl/tla/elan

Erickson, F. (2012). Definition and analysis of data from videotape: Some research procedures and their

rationales. In Green, J.L., Green, J., Camilli, G., Camilli, G., Elmore, P.B., & Elmore, P. (Eds.),

Handbook of Complementary Methods in Education Research, (3rd ed.). Routledge. 177–191.

https://doi.org/10.4324/9780203874769

Fenwick, T. (2011). Reading educational reform with actor network theory: Fluid spaces, otherings, and ambivalences. Educational Philosophy and Theory, 43(SUPPL. 1), 114–134.

https://doi.org/10.1111/j.1469-5812.2009.00609.x

Fenwick, T., & Edwards, R. (2010). Actor–Network Theory in Education.

https://doi.org/10.4324/9780203849088

Fruchterman, T. M., & Reingold, E. M. (1991). Graph drawing by force-directed placement. Software: Practice and Experience, 21 (11), 1129–1164. https://doi.org/10.1002/spe.4380211102

Gamble, C. N., Hanan, J. S., & Nail, T. (2019). What is new materialism? Angelaki: Journal of the Theoretical Humanities, 24 (6), 111–134. https://doi.org/10.1080/0969725X.2019.1684704

González-Howard, M. (2019). Exploring the utility of social network analysis for visualizing interactions during argumentation discussions. Science Education, 103 (3), 503–528.

https://doi.org/10.1002/sce.21505

Heritage, J. (1984). Garfinkel and Ethnomethodology. Cambridge: Polity Press.

Hetherington, L., Hardman, M., Noakes, J., & Wegerif, R. (2018). Making the case for a material-dialogic approach to science education. Studies in Science Education, 54 (2), 141–176.

https://doi.org/10.1080/03057267.2019.1598036

Hetherington, L., & Wegerif, R. (2018). Developing a material-dialogic approach to pedagogy to guide science teacher education. Journal of Education for Teaching: JET, 44 (1), 27–43.

https://doi.org/10.1080/02607476.2018.1422611

Jordan, B., & Henderson, A. (1995). Interaction Analysis: foundations and practice. Journal of the Learning Sciences, 4(1), 39–103. https://doi.org/10.1207/s15327809jls0401_2

Juuti, K., Lavonen, J., Salonen, V., Salmela-Aro, K., Schneider, B., & Krajcik, J. (2021). A teacher– researcher partnership for professional learning: co-designing project-based learning units to increase student engagement in science classes. Journal of Science Teacher Education, 32:6, 625-641. https://doi.org/10.1080/1046560X.2021.1872207

Knoke, D., & Yang, S. (2008). Social Network Analysis. Los Angeles, CA; London: SAGE Publications, Inc. https://dx.doi.org/10.4135/9781412985864

Koponen, I. T., & Mäntylä, T. (2020). Editorial: Networks applied in science education research. Education Sciences, 10 (5), 142. https://doi.org/10.3390/educsci10050142

Koponen, I. T., & Nousiainen, M. (2019). Pre-service teachers’ knowledge of relational structure of physics concepts: finding key concepts of electricity and magnetism. Education Sciences, 9(1), [18]. https://doi.org/10.3390/educsci9010018

Krajcik, J. S., & Shin, N. (2014). Project-based learning. In The Cambridge Handbook of the Learning Sciences (pp. 275–297). Cambridge: Cambridge University Press.

https://doi.org/10.1017/CBO9781139519526

Latour, B. (2005). Reassembling the Social: An Introduction to Actor-Network-Theory. Oxford; New York: Oxford University Press. https://doi.org/2027/heb32135.0001.001

Latour, B., & Woolgar, S. (1986). Laboratory Life: The Construction of Scientific Facts. Princeton: Princeton University Press. https://doi.org/10.2307/j.ctt32bbxc

Martínez, A., Dimitriadis, Y., Rubia, B., Gómez, E., & De la Fuente, P. (2003). Combining qualitative evaluation and social network analysis for the study of classroom social interactions. Computers and Education, 41 (4), 353–368. https://doi.org/10.1016/j.compedu.2003.06.001

McDonnell, M.D., Yaveroglu, Ö. N., Schmerl, B. A., Iannella, N. & Ward, L. M. (2014).Motif-role- fingerprints: The building-blocks of motifs, clustering-coefficients and transitivities in directed networks. PLoS ONE, 9 (12). https://doi.org/10.1371/journal.pone.0114503

Milo, R., Shen-Orr, S., Itzkovitz, S., & Kashtan, N. (2002). Network motif: simple building blocks of complex networks. Science, 298 (5594), 824–827. https://doi.org/10.1126/science.298.5594.824

Milne, C. (2019). The materiality of scientific instruments and why it might matter to science education. In C. Milne & K. Scantlebury (eds.), Material Practice and Materiality: Too Long Ignored in Science Education (pp. 9–23). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-01974-7_2

Milne, & Scantlebury, K. (2019). Material Practice and Materiality: Too Long Ignored in Science Education. Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-030-01974-7

Moore, R. J. (2015). Automated transcription and conversation analysis. Research on Language and Social Interaction, 48(3), 253–270. https://doi.org/10.1080/08351813.2015.1058600

Nespor, J. (2002). Networks and contexts of freedom. Journal of Educational Change, 3(Ccl), 365–382.

https://doi.org/10.1023/A:1021281913741

Oshima, J., Oshima, R., & Saruwatari, S. (2020). Analysis of students’ ideas and conceptual artifacts in knowledge-building discourse. British Journal of Educational Technology, 51(4), 1308–1321. https://doi.org/10.1111/bjet.12961

Peixoto, T. P. (2014). The graph-tool python library. figshare.

https://doi.org/10.6084/m9.figshare.1164194

Schneider, B., Krajcik, J., Lavonen, J., & Salmela-Aro, K. (2020). Learning Science: The Value of Crafting Engagement in Science Environments. Yale University Press.

https://doi.org/10.12987/9780300252736

Shaffer, D. W., Hatfield, D., Svarovsky, G. N., Nash, P., Nulty, A., Bagley, E., … Mislevy, R. (2009).

Epistemic network analysis: a prototype for 21st-century assessment of learning. International

Journal of Learning and Media, 1(2), 33–53. https://doi.org/10.1162/ijlm.2009.0013

Turkkila, M., & Lommi, H. (2020). Student participation in online content-related discussion and its relation to students’ background knowledge. Education Sciences, 10 (4), 106.

https://doi.org/10.3390/educsci10040106

Vicsek, L., Király, G., & Kónya, H. (2016). Networks in the social sciences. Corvinus Journal of Sociology and Social Policy, 7(2). https://doi.org/10.14267/CJSSP.2016.02.04

Wagner, S., Kok, K., & Priemer, B. (2020). Measuring characteristics of explanations with element maps.

Education Sciences, 10 (2). https://doi.org/10.3390/educsci10020036

Wasserman, S. (1994). Social Network Analysis: Methods and Applications. Cambridge: Cambridge University Press. https://doi.org/10.1017/CBO9780511815478

Yun, E., & Park, Y. (2018). Extraction of scientific semantic networks from science textbooks and comparison with science teachers’ spoken language by text network analysis. International Journal of Science Education, 40 (17), 2118–2136. https://doi.org/10.1080/09500693.2018.1521536

Zweig, K. A. (2016). Network Analysis Literacy: A Practical Approach to the Analysis of Networks. Vienna: Springer. https://doi.org/10.1007/978-3-7091-0741-6