Network Analysis as a Research Methodology in Science Education Research

Authors

  • Jesper Bruun Vydavatelství Pedagogické fakulty Univerzity Karlovy v Praze, Česká republika
  • Robert Evans

DOI:

https://doi.org/10.14712/23362189.2017.1026

Abstract

Abstract: With three examples, we explore diff erent ways of conceptualizing networks of nodes and links as educationally relevant entities. We show how one can analyse networks as they evolve over time â the dynamics of networks â and how one can model dynamic processes on networks. We also explain how networks can have both visual and mathematical properties that make them tractable as a way of generating knowledge about relational data. We suggest how a theory that emphasizes relational aspects might be developed using networks by providing an example about social networks, where we explain the generation of the theory-like structures rules of interaction, which are meant to have explanatory power. Likewise, we make interpretations of student discussions that are shaped in part by the algorithm we use to create discussion maps as well as by the changes we make to the transcript. Th e article illustrates how in a teacher-student dialogue, the structure of the network shapes the way we comprehend and talk about discussion networks. All these are dependent on the relational character of networks and would not be the same without that perspective. In other words, the theoretical positions we develop are shaped by the nature of networks. We conclude the article by discussing three developments, which need to occur to realize the potential of using network analysis in educational research.

Keywords: network analysis, dynamic, relational, methodological tool.

References

Artigue, M., & Winsløw, C. (2010). International comparative studies on mathematics education: A viewpoint from the anthropological theory of didactics. Recherches en didactique des mathématiques, 30(1), 47-82.

Barabási, A. L. (2016). Network science. Cambridge University Press.

Bastian, M., Heymann, S., & Jacomy, M. (2009). Gephi: an open source software for exploring and manipulating networks. ICWSM, 8, 361-362.

Bohlin, L., Edler, D., Lancichinetti, A., & Rosvall, M. (2014). Community detection and visualization of networks with the map equation framework. In Y. Dink, R. Rousseau, & D. Wolfram (Eds.), Measuring Scholarly Impact (pp. 3-34). Cham: Springer.

https://doi.org/10.1007/978-3-319-10377-8_1

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.

https://doi.org/10.1191/1478088706qp063oa

Brewe, E. (2008). Modeling theory applied: Modeling instruction in introductory physics. American Journal of Physics, 76(12), 1155-1160.

https://doi.org/10.1119/1.2983148

Brewe, E., Kramer, L., & Sawtelle, V. (2012). Investigating student communities with network analysis of interactions in a physics learning center. Physical Review Special Topics â Physics Education Research, 8(1), 010101.

https://doi.org/10.1103/PhysRevSTPER.8.010101

Bruun, J. (2012). Networks in Physics Education Research: A Theoretical, Methodological, and Didactical Explorative Study (Doctoral dissertation). Department of Science Education, University of Copenhagen.

Bruun, J. (2016). Networks as integrated in research methodologies in PER. In D. Jones, L. Ding, & A. Traxler (Eds.), 2016 PERC Proceedings (pp. 11-17). Sacramento, CA:American Association of Physics Teachers.

https://doi.org/10.1119/perc.2016.plenary.002

Bruun, J., & Brewe, E. (2013). Talking and learning physics: Predicting future grades from network measures and Force Concept Inventory pretest scores. Physical Review Special Topics â Physics Education Research, 9(2), 020109.

https://doi.org/10.1103/PhysRevSTPER.9.020109

Bruun, J., & Bearden, I. G. (2014). Time development in the early history of social networks: Link stabilization, group dynamics, and segregation. PLoS ONE, 9(11), e112775.

https://doi.org/10.1371/journal.pone.0112775

PMid:25402449 PMCid:PMC4234624

Buchenroth-Martin, C., DiMartino, T., & Martin, A. P. (2017). Measuring student interactions using networks: Insights into the learning community of a large active learning course. Journal of College Science Teaching, 46(3), 90.

https://doi.org/10.2505/4/jcst17_046_03_90

Clement, J. (1993). Using bridging analogies and anchoring intuitions to deal with students' preconceptions in physics. Journal of Research in Science Teaching, 30(10), 1241-1257.

https://doi.org/10.1002/tea.3660301007

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. Complex Systems, 1695(5), 1-9.

Daly, A. J. (Ed.). (2010). Social network theory and educational change (Vol. 8). Cambridge, MA: Harvard Education Press.

PMCid:PMC2918523

Davis, B., & Sumara, D. (2006). Complexity and education. New York: Routledge.

Deci, E. L., & Ryan, R. M. (2011). Self-determination theory. Handbook of Theories of Social Psychology, 1(2011), 416-433.

PMCid:PMC3162229

diSessa, A. A. (2002). Why "conceptual ecology" is a good idea. In Reconsidering conceptual change: Issues in theory and practice (pp. 28-60). Dordrecht: Springer.

https://doi.org/10.1007/0-306-47637-1_2

Dolin, J., Bruun, J., Nielsen, S. S., Jensen, S. B., & Nieminen, P. (2018). The structured assessment dialogue. In J. Dolin & R. Evans (Eds.), Transforming Assessment (pp. 109-140). Cham: Springer.

https://doi.org/10.1007/978-3-319-63248-3

https://doi.org/10.1007/978-3-319-63248-3_5

Fauconnier, G., & Turner, M. (2002). The way we think: Conceptual blending and the mind's hidden complexities. New York: Basic Books.

Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Cambridge University Press.

Forsman, J., Moll, R., & Linder, C. (2014). Extending the theoretical framing for physics education research: An illustrative application of complexity science. Physical Review Special Topics-Physics Education Research, 10(2), 020122.

https://doi.org/10.1103/PhysRevSTPER.10.020122

Goertzen, R. M., Brewe, E., & Kramer, L. (2013). Expanded markers of success in introductory university physics. International Journal of Science Education, 35(2), 262-288.

https://doi.org/10.1080/09500693.2012.718099

Grunspan, D. Z., Wiggins, B. L., & Goodreau, S. M. (2014). Understanding classrooms through social network analysis: A primer for social network analysis in education research. CBE-Life Sciences Education, 13(2), 167-178.

https://doi.org/10.1187/cbe.13-08-0162

PMid:26086650 PMCid:PMC4041496

Gregorcic, B., Planinsic, G., & Etkina, E. (2017). Doing science by waving hands: Talk, symbiotic gesture, and interaction with digital content as resources in student inquiry. Physical Review Physics Education Research, 13(2), 020104.

https://doi.org/10.1103/PhysRevPhysEducRes.13.020104

Harrison, C., Constantinou, C. P., Correia, C. F., Grangeat, M., Hähkiöniemi, M., Livitzis, M., ... Viiri, J. (2018). Assessment on-the-fly: Promoting and collecting evidence of learning through dialogue. Transforming Assessment (pp. 83-107). Cham: Springer.

PMid:29322470

Lemke, J. L. (1990). Talking science: Language, learning, and values. Norwood: Ablex.

Lijnse, P. & Klaassen, K. (2004). Didactical structures as an outcome of research on teaching–learning sequences? International Journal of Science Education, 26(5), 537-554.

https://doi.org/10.1080/09500690310001614753

Lindahl, M., Bruun, J., & Linder, C. (2016). Integrating text-mining, network analysis and thematic discourse analysis to produce maps of student discussions about sustainability. In PERC 2016.

Maslov, S., Sneppen, K., & Zaliznyak, A. (2004). Detection of topological patterns in complex networks: correlation profile of the internet. Physica A: Statistical Mechanics and its Applications, 333, 529-540.

https://doi.org/10.1016/j.physa.2003.06.002

Masucci, A. P., & Rodgers, G. J. (2006). Network properties of written human language. Physical Review E, 74(2), 026102.

https://doi.org/10.1103/PhysRevE.74.026102

PMid:17025498

Macfadyen, L. P., & Dawson, S. (2010). Mining LMS data to develop an "early warning system" for educators: A proof of concept. Computers & education, 54(2), 588-599.

https://doi.org/10.1016/j.compedu.2009.09.008

Podolefsky, N. S., & Finkelstein, N. D. (2007). Analogical scaffolding and the learning of abstract ideas in physics: An example from electromagnetic waves. Physical Review Special Topics â Physics Education Research, 3(1), 010109.

https://doi.org/10.1103/PhysRevSTPER.3.010109

R Core Team (2016). R: A language and environment for statistical computing. Retrieved form www.R-project.org/

Rosvall, M., & Bergstrom, C. T. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4), 1118-1123.

https://doi.org/10.1073/pnas.0706851105

PMid:18216267 PMCid:PMC2234100

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

Sneppen, K., Trusina, A., & Rosvall, M. (2005). Hide-and-seek on complex networks. EPL (Europhysics Letters), 69(5), 853.

https://doi.org/10.1209/epl/i2004-10422-0

Social network analysis software (n.d.). In Wikipedia. Retrieved February 7, 2018, from https://en.wikipedia.org/wiki/Social_network_analysis_software

Wenger, E. (1998). Communities of practice: Learning, meaning, and identity. Cambridge University Press.

https://doi.org/10.1017/CBO9780511803932

Wood, L. A., & Kroger, R. O. (2000). Doing discourse analysis: Methods for studying action in talk and text. Thousand Oaks, CA: Sage.

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Published

2018-08-19

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Methodological paper