At the midpoint of Spring term in my physics class at Drexel University, I had individual meetings with each of the students. It was only 10 minutes, just an opportunity to make sure I had a conversation with every student to check in on their progress and mind set during the COVID-19 quarantine. In response to the question, ‘How is the term going?’ one student said, “It is fine, I am doing fine. Classes are fine, my grades are good. But I miss the other students. Usually after class we would go eat breakfast together and talk about physics.”  

I suspect many of the students are experiencing the same sort of isolation and their learning is suffering as a result. The role of social interaction is important for a variety of learning related outcomes (Brewe, 2018). Tinto (1987, 1997) proposed a student’s sense of academic and social integration into the fabric of a university was key to understanding persistence, while Crisp et al. (2009) further elaborated on this by pointing out community building among students is fundamental in Hispanic-serving institutions. 

Studying the roles networks play in learning is rooted in the theoretical and empirical results from Tinto (1987, 1997), Crisp et al. (2009) and Nora (1987) as well as the results of Freeman et al. (2014), who demonstrated the role of active learning in STEM classes. What I find common across these results is the focus on students discussing, interacting and socialising. Research studies that apply social network analysis, a method with strong theoretical underpinnings, are highlighting the role of social interaction in classes. Bruun & Brewe (2013) found students' future grades could be predicted by network metrics based on who they talked to in class socially (student conversations did not focus on academic topics). Vargas et al. (2018) positively correlated network centrality with grades across upper division physics classes. Williams et al. (2019) showed how student social networks evolve in an introductory physics class, and Zwolak et al. (2017, 2018) demonstrated these same networks predict retention and persistence by students in future classes. 

Social network analysis, as applied to learning, identifies latent variables related to the ways in which students interact with each other, and with faculty and staff. It provides access to the complex systems that represent education and learning. These networks should not be expected to be limited to students either. Quardokus & Henderson (2015) and Addis et al. (2013) reported faculty networks are critical to understanding how changes might happen in teaching. 

Learning is not a socially isolated endeavor. The notion of a solitary genius is a myth and a dangerous one at that. Learning happens as students (or learners more broadly) discuss, question, engage, describe, explain and argue. It is a social process. It involves interactions that include disciplinary content but also, builds relationships. Which is why when my student said she missed her classmates and eating breakfast together, I tried to reassure her that eventually she’ll return to eating breakfast with her friends and re-establish the networks that support her learning. 

References

Addis, E. A., Quardokus, K. M., Bassham, D. C., Becraft, P. W., Boury, N., Coffman, C. R., ... & Powell-Coffman, J. A. (2013). Implementing pedagogical change in introductory biology courses through the use of faculty learning communities. Journal of College Science Teaching, 43 (2), 22 - 29.

Brewe, E. (2018). The roles of engagement: Network Analysis in Physics Education Research. Getting Started in Physics Education Research. https://www.compadre.org/Repository/document/ServeFile.cfm?ID=14725&DocID=4886

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.

Crisp, G., Nora, A., & Taggart, A. (2009). Student Characteristics, Pre-College, College, and Environmental Factors as Predictors of Majoring in and Earning a STEM Degree: An Analysis of Students Attending a Hispanic Serving Institution. American Educational Research Journal, 46 (4), 924 - 942.

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, 111 (23), 8410 - 8415.

Nora, A. (1987). Determinants of retention among Chicano college students: A structural model. Research in higher education, 26 (1), 31 - 59.

Quardokus, K., & Henderson, C. (2015). Promoting instructional change: using social network analysis to understand the informal structure of academic departments. Higher Education, 70 (3), 315 - 335. 

Tinto, V. (1987). Leaving college: Rethinking the causes and cures of student attrition. University of Chicago Press, 5801 S. Ellis Avenue, Chicago, IL 60637. 

Tinto, V. (1997). Classrooms as communities: Exploring the educational character of student persistence. The Journal of Higher Education, 68 (6), 599 - 623.

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.

Williams, E. A., Zwolak, J. P., Dou, R., & Brewe, E. (2019). Linking engagement and performance: The social network analysis perspective. Physical Review Physics Education Research, 15 (2), 020150.

Zwolak, J. P., Dou, R., Williams, E. A., & Brewe, E. (2017). Students’ network integration as a predictor of persistence in introductory physics courses. Physical Review Physics Education Research, 13 (1), 010113.

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.