Networks and Learning: A View from Physics
DOI:
https://doi.org/10.18608/jla.2022.7669Keywords:
network analysis, physics education research, gender, invited paperAbstract
Like learning analytics, physics education research is a relatively young field that draws on perspectives from multiple disciplines. Network analysis has an even more heterodox perspective, with roots in mathematics, sociology, and, more recently, computer science and physics. This paper reviews how network analysis has been used in physics education research and how it connects to some of the work in this special issue. Insights from physics education research suggest combining social and interaction networks with other data sources and looking for finer-grained details to use in constructing networks, and learning analytics is promising for both avenues. The discussion ends by looking at the complications with incorporating gender into network analysis, and finally the possibilities for the future.
References
Beichner, R. J., Saul, J. M., Abbott, D. S., Morse, J. J., Deardorff, D. L., Allain, R. J., ... Risley, J. S. (2007). The student-centered activities for large enrollment undergraduate programs (SCALE-UP) project. In E. F. Redish & P. J. Cooney (Eds.), Research-Based Reform of University Physics (Vol. 1). American Association of Physics Teachers.
Bing, T. J., & Redish, E. F. (2009). Analyzing problem solving using math in physics: Epistemological framing via warrants. Physical Review Special Topics: Physics Education Research, 5(2), 020108. https://doi.org/10.1103/PhysRevSTPER.5.020108
Blue, J., Traxler, A., & Cochran, G. (2019). Resource letter: GP-1: Gender and physics. American Journal of Physics, 87(8), 616. https://doi.org/10.1119/1.5114628
Bodin, M. (2012). Mapping university students’ epistemic framing of computational physics using network analysis. Physical Review Special Topics: Physics Education Research, 8(1), 010115. https://doi.org/10.1103/PhysRevSTPER.8.010115
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. H., & O’Brien, G. E. (2010). Changing participation through formation of student learning communities. AIP Conference Proceedings, 1289, 85–88. http://dx.doi.org/10.1063/1.3515255
Brewe, E., Kramer, L., & Sawtelle, V. (2012). Investigating student communities with network analysis of interactions in a physics learning center. Physical Review Special Topics: Physics Education Research, 8(1), 010101. https://doi.org/10.1103/PhysRevSTPER.8.010101
Brewe, E., Bruun, J., & Bearden, I. G. (2016). Using module analysis for multiple choice responses: A new method applied to force concept inventory data. Physical Review Physics Education Research, 12(2), 020131. https://doi.org/10.1103/PhysRevPhysEducRes.12.020131
Bruun, J. (2016). Networks as integrated in research methodologies in PER. Proceedings of the 2016 Physics Education Research Conference (PERC 2016), 20–21 July 2016, Sacramento, CA, USA (pp. 11–17). https://doi.org/10.1119/perc.2016.plenary.002
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
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: Physics Education Research, 9(2), 020109. https://doi.org/10.1103/PhysRevSTPER.9.020109
Chi, M. T., & Wylie, R. (2014). The ICAP framework: Linking cognitive engagement to active learning outcomes. Educational Psychologist, 49(4), 219–243. https://doi.org/10.1080/00461520.2014.965823
Commeford, K., Brewe, E., & Traxler, A. (2021). Characterizing active learning environments in physics using network analysis and classroom observations. Physical Review Physics Education Research, 17(2), 020136. https://doi.org/10.1103/PhysRevPhysEducRes.17.020136
De Domenico, M., Lancichinetti, A., Arenas, A., & Rosvall, M. (2015). Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems. Physical Review X, 5(1), 011027. https://doi.org/10.1103/PhysRevX.5.011027
Docktor, J. L., & Mestre, J. P. (2014). Synthesis of discipline-based education research in physics. Physical Review Special Topics: Physics Education Research, 10(2), 020119. https://doi.org/10.1103/physrevstper.10.020119
Dou, R., Brewe, E., Zwolak, J. P., Potvin, G., Williams, E. A., & Kramer, L. H. (2016). Beyond performance metrics: Examining a decrease in students’ physics self-efficacy through a social networks lens. Physical Review Physics Education Research, 12(2), 020124. https://doi.org/10.1103/PhysRevPhysEducRes.12.020124
Doucette, D., Clark, R., & Singh, C. (2020). Hermione and the secretary: How gendered task division in introductory physics labs can disrupt equitable learning. European Journal of Physics, 41(3), 035702. https://doi.org/10.1088/1361-6404/ab7831
Elby, A. (2010). Getting started with research on epistemologies and expectations. In C. Henderson & K. A. Harper (Eds.), Getting Started in PER (1st ed., Vol. 2). http://www.compadre.org/per/items/detail.cfm?ID=10578
Fernandez, T., Godwin, A., Doyle, J., Verdin, D., Boone, H., Kirn, A., ... Potvin, G. (2016). More comprehensive and inclusive approaches to demographic data collection. Proceedings of the Annual Conference of the American Society for Engineering Education (ASEE 2016), 26–29 June 2016, New Orleans, LA, USA. IEEE. (p. 14546). https://docs.lib.purdue.edu/enegs/60
Freeman, L. C. (1978). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239. https://doi.org/10.1016/0378-8733(78)90021-7
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences of the United States of America, 111, 8410–8415. https://doi.org/10.1073/pnas.1319030111
Gavrin, A., & Lindell, R. S. (2017). Connecting students’ homework to their participation in a course-based social network. Proceedings of the Annual Conference of the American Society for Engineering Education (ASEE 2017), 24–28 June 2017, Columbus, OH, USA. IEEE. https:// peer.asee.org/28070
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
Hake, R. R. (1998). Interactive-engagement versus traditional methods: A six-thousand-student survey of mechanics test data for introductory physics courses. American Journal of Physics, 66(1), 64–74. https://doi.org/10.1119/1.18809
Hazari, Z., Sonnert, G., Sadler, P. M., & Shanahan, M.-C. (2010). Connecting high school physics experiences, outcome expectations, physics identity, and physics career choice: A gender study. Journal of Research in Science Teaching, 47(8), 978–1003. https://doi.org/10.1002/tea.20363
Hsu, L., Brewe, E., Foster, T. M., & Harper, K. A. (2004). Resource letter RPS-1: Research in problem solving. American Journal of Physics, 72(9), 1147. https://doi.org/10.1119/1.1763175
Joksimović, S., Dowell, N., Poquet, O., Kovanović, V., Gašević, D., Dawson, S., & Graesser, A. C. (2018). Exploring development of social capital in a CMOOC through language and discourse. The Internet and Higher Education, 36, 54–64. https://doi.org/10.1016/j.iheduc.2017.09.004
Joksimović, S., Jovanović, J., Kovanović, V., Gašević, D., Milikić, N., Zouaq, A., & Van Staalduinen, J. P. (2019). Comprehensive analysis of discussion forum participation: From speech acts to discussion dynamics and course outcomes. IEEE Transactions on Learning Technologies, 13(1), 38–51. https://doi.org/10.1109/tlt.2019.2916808
Kanim, S., & Cid, X. C. (2020). Demographics of physics education research. Physical Review Physics Education Research, 16(2), 020106. https://doi.org/10.1103/PhysRevPhysEducRes.16.020106
Kelley, P., Gavrin, A., & Lindell, R. S. (2018, March). Text mining online discussions in an introductory physics course. Proceedings of the 2017 Physics Education Research Conference (PERC 2017), 26–27 July 2017, Cincinnati, OH, USA (pp. 216–219). https://www.compadre.org/per/items/detail.cfm?ID=14608
Kohl, P. B., & Finkelstein, N. D. (2008). Patterns of multiple representation use by experts and novices during physics problem solving. Physical Review Special Topics: Physics Education Research, 4(1), 010111. https://doi.org/10.1103/PhysRevSTPER.4.010111
Koponen, I. T., & Pehkonen, M. (2010). Coherent knowledge structures of physics represented as concept networks in teacher education. Science & Education, 19(3), 259–282. https://doi.org/10.1007/s11191-009-9200-z
Kortemeyer, G. (2006). An analysis of asynchronous online homework discussions in introductory physics courses. American Journal of Physics, 74(6), 526–536. https://doi.org/10.1119/1.2186684
Madsen, A., McKagan, S. B., & Sayre, E. C. (2017). Resource letter RBAI-1: Research-based assessment instruments in physics and astronomy. American Journal of Physics, 85(4), 245. https://doi.org/10.1119/1.4977416
Mazur, E. (1997). Peer instruction: A user’s manual. Prentice Hall. Retrieved from https://www.pearson.com/us/higher-education/program/Mazur-Peer-Instruction-A-User-s-Manual/PGM174484.html
McDermott, L. C. (2001). Oersted medal lecture 2001: Physics education research: The key to student learning. American Journal of Physics, 69(11), 1127. https://doi.org/10.1119/1.1389280
McDermott, L. C., & Shaffer, P. S. (1992). Research as a guide for curriculum development: An example from introductory electricity. Part I: Investigation of student understanding. American Journal of Physics, 60, 994–1003. https://doi.org/10.1119/1.17003
McDermott, L. C., Shaffer, P. S., & University of Washington Physics Education Group. (1998). Tutorials in introductory physics. Prentice Hall. Retrieved from https://www.pearson.com/us/higher-education/product/Mc-Dermott-Tutorials-In-Introductory-Physics-and-Homework-Package/9780130970695.html
Meltzer, D. E., & Thornton, R. K. (2012). Resource letter ALIP-1: Active-learning instruction in physics. American Journal of Physics, 80(6), 478–496. https://doi.org/10.1119/1.3678299
Myers, C., Fox, E., Traxler, A., & Gavrin, A. (2019). Quantifying the linguistic persistence of high and low performers in an online student forum. In M. B. Bennett, S. Wolf, & Y. Cao (Eds.), Proceedings of the 2019 Physics Education Research Conference (PERC 2019), 24–25 July 2019, Provo, UT, USA (pp. 402–407). https://doi.org/10.1119/perc.2019.pr.Myers
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245–251. https://doi.org/10.1016/j.socnet.2010.03.006
Otero, V. (2003). Cognitive processes and the learning of physics, Part I: The evolution of knowledge from a Vygotskian perspective. In E. Redish & M. Vicentini (Eds.), Proceedings of the International School of Physics “Enrico Fermi” Course CLVI (Vol. 156, pp. 409–446). IOS Press.
Poquet, O., Tupikina, L., & Santolini, M. (2020). Are forum networks social networks? A methodological perspective. Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK ’20), 23–27 March 2020, Frankfurt, Germany (pp. 366–375). https://doi.org/10.1145/3375462.3375531
Poquet, O., Saqr, M., & Chen, B. (2021). Recommendations for network research in learning analytics: To open a conversation. In O. Poquet, B. Chen, M. Saqr, & T. Hecking (Eds.), Proceedings of the NetSciLA 2021 Workshop: Using Network Science in Learning Analytics: Building Bridges towards a Common Agenda (NetSciLA 2021), 12 April 2021, Newport Beach, CA, USA (virtual). http://ceur-ws.org/Vol-2868/
Rasmussen, K. C., Maier, E., Strauss, B. E., Durbin, M., Riesbeck, L., Wallach, A., ... Erena, A. (2019, July). The nonbinary fraction: Looking towards the future of gender equity in astronomy. https://arxiv.org/abs/1907.04893
Saqr, M., & López-Pernas, S. (2022). The curious case of centrality measures: A large-scale empirical investigation. Journal of Learning Analytics, 9(1). https://doi.org/10.18608/jla.2022.7415
Sawtelle, V., Brewe, E., & Kramer, L. H. (2012). Exploring the relationship between self-efficacy and retention in introductory physics. Journal of Research in Science Teaching, 49(9), 1096–1121. https://doi.org/10.1002/tea.21050
Slaton, A. E., & Pawley, A. L. (2015). The power and politics of STEM research design: Saving the “small N.” Proceedings of the 122nd Annual Conference of the American Society for Engineering Education (ASEE 2015), 13 June 2015, Seattle, WA, USA. IEEE. https://peer.asee.org/24901
Smith, J. A., & Moody, J. (2013). Structural effects of network sampling coverage I: Nodes missing at random. Social Networks, 35(4), 652–668. https://doi.org/10.1016/j.socnet.2013.09.003
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
Traxler, A. (2015). Community structure in introductory physics course networks. In A. D. Churukian, D. L. Jones, & L. Ding (Eds.), Proceedings of the 2015 Physics Education Research Conference (PERC 2015), 29–30 July 2015, College Park, MD, USA (pp. 331–334). https://doi.org/10.1119/perc.2015.pr.078
Traxler, A. L., Cid, X. C., Blue, J., & Barthelemy, R. (2016). Enriching gender in physics education research: A binary past and a complex future. Physical Review Physics Education Research, 12(2), 020114. https://doi.org/10.1103/PhysRevPhysEducRes.12.020114
Traxler, A., Gavrin, A., & Lindell, R. (2018a). Networks identify productive forum discussions. Physical Review Physics Education Research, 14(2), 020107. https://doi.org/10.1103/PhysRevPhysEducRes.14.020107
Traxler, A., Henderson, R., Stewart, J., Stewart, G., Papak, A., & Lindell, R. (2018b). Gender fairness within the force concept inventory. Physical Review Physics Education Research, 14(1), 010103. https://doi.org/10.1103/PhysRevPhysEducRes.14.010103
Traxler, A. L., Suda, T., Brewe, E., & Commeford, K. (2020). Network positions in active learning environments in physics. Physical Review Physics Education Research, 16, 020129. https://doi.org/10.1103/PhysRevPhysEducRes.16.020129
Trowbridge, D. E., & McDermott, L. C. (1980). Investigation of student understanding of the concept of velocity in one dimension. American Journal of Physics, 48(12), 1020–1028. https://doi.org/10.1119/1.12298
Tuminaro, J., & Redish, E. F. (2007). Elements of a cognitive model of physics problem solving: Epistemic games. Physical Review Special Topics: Physics Education Research, 3(2), 020101. https://doi.org/10.1103/PhysRevSTPER.3.020101
Vargas, D. L., Bridgeman, A. M., Schmidt, D. R., Kohl, P. B., Wilcox, B. R., & Carr, L. D. (2018). Correlation between student collaboration network centrality and academic performance. Physical Review Physics Education Research, 14(2), 020112. https://doi.org/10.1103/PhysRevPhysEducRes.14.020112
Vignery, K., & Laurier, W. (2020). A methodology and theoretical taxonomy for centrality measures: What are the best centrality indicators for student networks? PLOS ONE, 15(12), e0244377. https://doi.org/10.1371/journal.pone.0244377
Wells, J., Henderson, R., Traxler, A., Miller, P., & Stewart, J. (2020). Exploring the structure of misconceptions in the force and motion conceptual evaluation with modified module analysis. Physical Review Physics Education Research, 16(1), 010121. https://doi.org/10.1103/PhysRevPhysEducRes.16.010121
Whitten, B. L., Foster, S. R., & Duncombe, M. L. (2003). What works for women in undergraduate physics? Physics Today, 56(9), 46–51. https://doi.org/10.1063/1.1620834
Wise, A. F., & Cui, Y. (2018). Learning communities in the crowd: Characteristics of content related interactions and social relationships in MOOC discussion forums. Computers & Education, 122, 221–242. https://doi.org/10.1016/j.compedu.2018.03.021
Woo, Y., & Reeves, T. C. (2007). Meaningful interaction in web-based learning: A social constructivist interpretation. The Internet and Higher Education, 10(1), 15–25. https://doi.org/10.1016/j.iheduc.2006.10.005
Yang, Y., Nainabasti, B., Brookes, D. T., & Brewe, E. (2015). A study of informal learning communities: A tale of two physics courses. In P. V. Engelhardt, A. D. Churukian, & D. L. Jones (Eds.), Proceedings of the 2014 Physics Education Research Conference (PERC 2014), 30–31 July 2014, Minneapolis, MN, USA (pp. 283–286). https://www.per-central.org/items/detail.cfm?ID=13506
Zwolak, J. P., Zwolak, M., & Brewe, E. (2018). Educational commitment and social networking: The power of informal networks. Physical Review Physics Education Research, 14(1), 010131. https://doi.org/10.1103/PhysRevPhysEducRes.14.010131
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2022 Journal of Learning Analytics
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
TEST
