To date, the use of social network analysis (SNA) in education has demonstrated numerous benefits reported in the research literature, such as: learning design (Lockyer et al., 2013), sense of community (Dawson, 2008), creativity potential (Dawson, Tan, & McWilliam, 2011), academic performance (Gašević et al., 2013), social presence (Kovanović et al., 2014) and understanding of MOOCs (Kovanović et al., 2014).
I want to briefly describe about these papers previously introduced by Professor Dragan in DALMOOC.
1.SNA and Learning Design
Lockyer et al. (2013) investigated how learning design influences learner’s actions (The full article is here). Lockyer and collegues describe very well learning design as:
“Learning design describes the sequence of learning tasks, resources, and supports that a teacher constructs for students over part of, or the entire, academic semester. A learning design captures the pedagogical intent of a unit of study. As such, learning designs provide a model for intentions in a particular learning context that can be used as a framework for design of analytics to support faculty in their learning and teaching decisions.
They used a case-based learning design to investigate how analytics tools of two types—checkpoint and process analytics— can help instructors to see what is happening inside of their classes and choose pedagogical strategies.
This is a very interesting article which the authors provide some SNA scenarios, such as: an instructor-centered network (quite common in many courses), a discussion dominated by one student and social network example indicating strong student peer interaction.
According to Professor Dragan one of the purposes of learning design is to provide strong student peer interaction but at the same time is to have the role of the instructor to start building the comfortable atmosphere so that they can easily interact with each other.
Lockyer and colleagues (Lockyer et al , 2013) also conclude “Establishing a conceptual framework for typical learning analytics patterns expected from particular learning designs can be considered an essential step in improving evaluation effectiveness and to build the foundation for pedagogical recommender systems in the future.”
2.SNA and Sense of Community
Is there an association between sense of community and network position?
This question was investigate by Shane Dawson in this paper. Dawson (2008) incorporates a mixed method approach utilising both quantitative and SNA centrality measures in order to evaluate an individual’s level of sense of community. Dawson (2008) states:
“The closeness and degree centrality measures also illustrate that students engaged with a greater number of learners report a higher level of sense of community than their less socially active peers.
3.SNA and Creative Potential
Is there any association between creativity capacity of students in general education contexts, and network brokers?
In a previous post, I mentioned Dawson’s article about creative potential. Dawson and colleagues (Dawson, Tan, and McWilliam, 2011) found significant association between the degree centrality and betweenness centrality of the students, while the closeness centrality was not significantly associated. Based on this study, Dawson and colleagues highlight that social network analysis (SNA) centrality calculations have a potential to provide insights related to the development of creativity of students. Of course, Dawson’s work offers many more details. The full article is here.
4.SNA and Academic Performance
Gašević and colleagues (Gašević et al., 2013) studied the association of Cross-Class Social Ties and Academic Performance (The full article can be found here). The principles of this work were based on ideas of Vygotsky (1978) who noted that higher levels of internalization are reached through social interaction. And also based in contemporary pedagogies, such as: learners active constructors of their knowledge (Adams, 2006) and collaborative learning (Johnson & Johnson , 2009).
The authors investigated two hypothesis:
“i) students’ social capital accumulated through their course progression is positively associated with their academic performance; and ii) students with more social capital have a significantly higher academic performance.
To assess the student social capital they calculated the following measures of centrality: Degree centrality, Closeness centrality, betweenness centrality and Eccentricity. As the outcomes this studies demonstrated a strong association between closeness, centrality, and eccentricity with the academic performance while betweenness centrality was not significantly associated.
5.SNA and Social Presence
Professor Dragan defines social presence as: “the way how learners project themselves socially and emotionally as real people in an online environment.”
Kovanović et al.(2014), in this work, explored the links between the Community of Inquiry (CoI) model and the SNA of student networks. The CoI model is rooted in the social constructivist philosophy, based on ideas of Dewey (1897). This model consists of three constructors, also known as presences: Cognitive Presence, Teaching Presence and Social Presence. The authors also highlight that this model is well-researched and widely accepted within the distance learning research community. The social presence in the CoI model is defined of three different dimension of communication: Affectivity, Interactivity and Cohesiveness.
The research questions investigated by Kovanović and colleagues (2014):
“What is the relationship between the students’ social capital, as captured by social network centrality measures, and students’ social presence, as defined by the three categories in the Community of Inquiry model?
The authors extracted the three network centrality measures: Betweenness centrality, Degree centrality (in and out-degree) and Closeness centrality. The results evealed that both in and out-degree centrality measures were significantly predicted by all the three categories of students’ social presence. On the other hand, betweenness was the best explained only by affective and interactivity categories in online discussion while cohesiveness messages did not show any association. They also propose for future work replication of their findings on a bigger sample and with more diverse courses from different subject matter domains.
6.SNA and Understanding of MOOCs
Kovanović et al. (2014) investigated patterns of interaction evolving from a socio- technical network in a connectivist Massive Open Online Course (cMOOC).The authors propose:
“facilitators have a significant role in shaping discussions and the course outline, learners may also have an important impact on how information flows and knowledge is constructed in such settings. At the same time, technological affordances such as hashtags, can have an important effect on how cMOOC participants find, share, aggregate, and connect information.
In order to investigate these proposes Kovanović and colleagues (2014) defined two research questions: “What is the influence of original course facilitators, course participants (ie., learners), and technological affordances on information flows in different stages of a cMOOC?” and “What are the major factors that influence the formation of communities of learners within the social network developed around a cMOOC?”.
The constructed network was analyzed using the following measures: Closeness centrality, Betweenness centrality, Authority weight, Hub weights, Weighted degree and Modularity components.
Once these networks are constructed, the authors performed network analysis by measuring centralities in order to determine who were the most influential nodes in the network as well as they performed a node analysis on the networks. As the results of this work, the topmost influential nodes measured by in degree centrality were actually hashtags or the students who emerged as course facilitators rather than original course facilitators. The full article is here.
Integration of Social Network Analysis in Gephi and Tableau Analysis (CCK11 dataset)
I exported the results of SNA(centrality and modularity) of the network blog in week 12 available in the CCK11 dataset from Gephi – via the Data Laboratory tab of Gephi – in the format (i.e., CSV) that can be imported into Tableau. And then I created a Dashboard 1 and 2. To keep it simple, relations with Twitter network are not presented.
My analysis showed, from Dashboard 1, that the nodes 3, 10,11 and 9 demonstrated a high level of influence (measured by degree) within the week 12. By measuring betweenness centrality, it’s revealed those that performed a critical role in brokering information among sub-communities. I also observed an interesting pattern: nodes (3,10,11 and 9) who were detected as key players by degree, also are the main brokers (measured by betweenness) and the strongest contributors (measured by out-degree).
These observations demonstrate a significant association between degree, betweenness and out-degree centrality (Centralities Dashboard 1), while the in-degree and closeness centralities (Centralities Dashboard 2) were not significantly associated.
On the other hand, from Dashboard 2, my analysis also reveals that the node that has the highest level of influence and the strongest contributor (node 3) has a low in-degree centrality. The nodes 19 and 11 are the most reported by others members (measured by In-degree) followed by nodes 10, 18 and 17.
A modularity algorithm was applied resulting in 5 communities (colored by Modularity Class) for week 12. Having in mind that communities – also called clusters – are groups of vertices which probably share common properties and/or play similar roles within a community (Fortunato, 2010). The SNA (Dashboard 1 – bottom left) shows the structures of the communities. From this, it’s possible to identify a pattern – the most populated communities (red, green and purple) had one or two central nodes (sized by degree and labeled by Id). They play an important rule – they can influence the information flow in the network (Kovanović et al., 2014).
Centralities Dashboard 1
Centralities Dashboard 2
I also found some limitations in my studies: the nodes labeled as “Id” for each student is coded within “Data Laboratory” of Gephi. Ok, I could set “Label” on nodes and compare centralities between week 6 and week 12. However, the column “Label” from both networks represents long strings of alphanumeric. So it’s not easy to visualize. I had some ideas but not efficiently. If you figure out a way to solve this issue in a useful manner, feel free to leave a comment. Feedback is always more than welcome!
Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), 224–238 (full text).
Dawson, S., Tan, J. P. L., & McWilliam, E. (2011). Measuring creative potential: Using social network analysis to monitor a learners’ creative capacity. Australasian Journal of Educational Technology, 27(6), 924-942 (full text).
Dewey, J. (1897).My pedagogical creed. School Journal, 54(3):77– 80.
Fortunato, S. (2010). Community detection in graphs. Physics Reports, 486(3-5), 75-174
Gašević, D., Zouaq, A., Jenzen, R. (2013). Choose your Classmates, your GPA is at Stake!’ The Association of Cross-Class Social Ties and Academic Performance. American Behavioral Scientist, 57(10), 1459-1478. doi: 10.1177/0002764213479362 (full text).
Kovanović, V., Joksimović, S., Gašević, D., Hatala, M., “What is the source of social capital? The association between social network position and social presence in communities of inquiry,” In Proceedings of 7th International Conference on Educational Data Mining – Workshops, London, UK, 2014 (full text).
Lockyer, L., Heathcote, E., & Dawson, S. (2013). Informing pedagogical action: Aligning learning analytics with learning design. American Behavioral Scientist, 57(10), 1439-1459, doi:10.1177/0002764213479367 (full text)
Skrypnyk, O., Joksimović, S. Kovanović, V., Gasevic, D., Dawson, S. (2014). Roles of course facilitators, learners, and technology in the flow of information of a cMOOC. British Journal of Educational Technology (submitted) (full text).
Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. (M. Cole, V. John-Steiner, S. Scribner, & E. E. Souberman, Eds.) Cambridge, Massachusetts: Harvard University Press.