Recently, I and one of my advisors Dr. Cassandra Scharber published a social network analysis (SNA) study titled “The influences of an experienced instructor’s discussion design and facilitation on an online learning community development: A social network analysis study” in *The Internet and Higher Education*. A short audioslide presentation of this overview can be found here. Please also read a brief introduction of SNA methods and my design of a visualization showcase app at my Github page. I have also been building up a network analytics R package to show my analysis process in this study; please find the package in my Github here.

In this study, we first characterized an effective online learning community as an environment with learners’ reciprocal interaction and continuous participation. Then, based on this philosophy, we argued that the **social network analysis (SNA) **method is an appropriate research method for studying online learning communities. We further proposed **an integrated network analysis framework** (refer to section 4.4 in this article ) using emerging network methods to analyze both one-mode and two-mode networks in online learning. This framework combined both emerging SNA measures — Opsahl’s measures (Opsahl, 2009; Opsahl, 2013; Opsahl, 2015; Opsahl, Agneessens, & Skvoretz, 2010; Opsahl & Panzarasa, 2009), with more traditional measures — Butts’ measures (Butts, 2008; Butts, 2014; Butts, Hunter, Handcock, Bender-deMoll, & Horner, 2015). The analysis process was conducted via R programming and relevant packages (i.e., *tnet*, and *sna*). In addition, Marcos-García et al.’s (2015) DESPRO method, using centrality ranges to detect participatory roles, can be combined with Opsahl’s (2015) SNA centralities to make the results more accurate.

The research purpose for this study is *twofold*: we aim to provide methodological implications for using emerging social network analysis in online learning community research; and to provide practical implications for designing and facilitating discussions that can foster online learning communities.

SNA results showed the students gradually formed an interactive, cohesive and equally-distributed learning community duing *class-level and group-level discussions . *The instructor, overall, played a

*facilitator*role in this community; yet her participatory roles varied within different discussions during different time frames. Her participatory role evolved from a

*guide*in the first class-level discussion, to varying roles, i.e., a

*facilitator*, an

*observer,*and a

*collaborator*within different group discussions at the middle stages of the course, and to an

*observer*in the course’s later stages.

Two important implications of this study are: (1) practical implications for designing and facilitating discussions that can foster online learning communities were proposed. Strategies include: design of a structural interweaving of class-level and group-level discussions; use of base groups at the early stage of an online course; integration of opportunistic collaboration groups with “fixed” group configuration; the instructor’s leadership role in the early stage, role changes in terms of different group situations, and relinquishment of authority in the middle and late stages.

(2) methodological implications for studying online learning communities were proposed. An integrated social network analysis framework for one-mode and two-mode network analysis as well as adapted instructors’ participatory role examination were proposed. Particularly, specific suggestions of using SNA measures in online community research that stress both interaction and participation were included in this framework. It is worth mention that Opsahl’s network measures combining both the effect of *the number of ties* and the effect of *tie weights* can offer a more robust method to analyze online learning communities. In addition, Butts et al. and his colleagues (2014, 2015)’ measures on *reciprocity,* *transitivity*, c*entralization* are also important measures. Basic statistics, such as student-student, student-instructor, and instructor-student *interaction frequency* are simple yet useful measures.

This study is a part of my dissertation research. For a more fun (and short) introduction of my dissertation study, please refer to my 3MT presentation. I hope you will find this SNA study interesting and useful for your own research. Feel free to contact me at ouyan064@umn.edu for further questions or details.

Ref:

Butts, C. T. (2008). Social network analysis: A methodological introduction. *Asian Journal of Social Psychology, 11*(1), 13-41. doi:10.1111/j.1467-839X.2007.00241.x

Butts, C. T. (2014). sna: Tools for social network analysis (version 2.3-2) [R package]. Retrieved from http://CRAN.R-project.org/package=sna

Butts, C. T., Hunter, D., Handcock, M., Bender-deMoll, S., & Horner, J. (2015). network: Classes for relational data (version 1.13.0) [R package]. Retrieved from https://cran.r-project.org/web/packages/network/index.html

Marcos-García, J. A., Martínez-Monés, A., & Dimitriadis, Y. (2015). DESPRO: A method based on roles to provide collaboration analysis support adapted to the participants in CSCL situations. *Computers & Education, 82*, 335-353. doi:10.1016/j.compedu.2014.10.027

Opsahl, T. (2009). *Structure and evolution of weighted networks* (Doctoral dissertation, University of London). Retrieved from http://toreopsahl.com/publications/thesis/

Opsahl, T. (2013). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. *Social Networks, 35*(2), 159-167. doi:10.1016/j.socnet.2011.07.001

Opsahl, T. (2015). tnet: Software for analysis of weighted, two-mode, and longitudinal networks (version 3.0.14) [R package]. Retrieved from https://cran.r-project.org/web/packages/tnet/index.html

Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. *Social Networks, 32*(3), 245–251. doi:10.1016/j.socnet.2010.03.006

Opsahl, T., & Panzarasa, P. (2009). Clustering in weighted networks. *Social Networks, 31*(2), 155-163. doi:10.1016/j.socnet.2009.02.002