Chapter 2. Literature Review
2.3. A new perspective: Learning as changes in positioning in forum discussion
2.3.4. Prior work on social relations
Social relations in MOOC discussion forums are usually examined using social network analysis (SNA) methods. SNA research often conceptualizes relationships in discussion forums as a network with participants as nodes and reply structures between them as ties (Dowell et al., 2015; Jiang, Fitzhugh, & Warschauer, 2014; Joksimović et al., 2016; Poquet & Dawson, 2016).
MOOC research has characterized discussant roles in information exchange based on structural similarity (sharing similar connections with similar others in the social network). For instance, Kellogg et al. (2014) identified learners who participated in discussions with similar patterns in a digital learning MOOC and a mathematics MOOC. For each course, a directed weighted social network was constructed based on the Direct Reply tie definition that constructs ties between people who have directly replied to each other’s posts. Regular equivalence algorithm was used to partition forum participants into groups based on the similarity of their ties to others with similar ties. Four participation patterns were identified: reciprocators who participated in at least one mutual exchange; networkers who gave and received responses but with different peers;
broadcasters who only initiated discussions; and the invisible who responded to others’
posts but did not receive any response. Reciprocators made up the largest proportion of learners in both courses. This approach to identifying roles in information exchange is particularly useful for mega networks. However, the reply structure in the social network is a high level structural proxy of the actual information flow pattern. In comparison analyzing the content of discussion messages can capture learners’ roles in information exchange more directly (e.g., Hecking, Chounta, & Hoppe, 2017).
Other MOOC research that adopted the SNA methods has found learner’s connectedness in the social network and the strength of their social connections can provide understanding of discussant roles that complement the content analysis approach.
Connectedness in the social network
Connectedness indicates how well-connected a node is in a social network. Learner’s connectedness can be quantified by node-level centrality properties, such as degree (number of direct connections a node has), betweenness (the number of times a node is part of the shortest path between two other nodes in the network), and
closeness (average of the shortest path from a node to all other nodes in the network, see Jiang, Fitzhugh, & Warschauer, 2014; Joksimović et al., 2016). Centrality properties are often associated with assumptions about influence, power, and privilege. For
instance, high in-degree (number of connections pointing to a node) indicates a prestige status and high out-degree (number of connections pointing from a node) indicates a hub status; high betweenness indicates a broker status; high closeness indicates easy access to resources (Jiang, Fitzhugh, & Warschauer, 2014; Joksimovic et al., 2016). Learner’s connectedness can also be presented graphically by a core–periphery structure. Social graphs for large social networks often consist of a small number of highly connected nodes at the core and a large number of less connected and isolated nodes in the periphery (Goggins et al., 2016).
Both degree and betweenness and the associated core-periphery structure have been used for characterizing roles in MOOC forums (Brinton et al., 2014; Dowell et al., 2015; Joksimovic et al., 2016; Poquet & Dawson, 2016). For instance, Poquet and Dawson (2016) investigated influential learners in an undirected weighted network (constructed based on thread copresence) for discussion forums in a solar energy MOOC. They clustered forum participants through k-means clustering using betweennenss (indexing the quantity of participated conversations) and clustering coefficient (number of triangles a node is in divided by number of triangles it could be in, indexing a learner’s level of embeddedness in different conversations). Two of the four clusters they detected demonstrated characteristics of influential members. One cluster consisted of 8 highly influential members who had very high betweenness and low clustering coefficient. They were likely to be community TAs. The other cluster consisted of 82 moderately influential members with moderate betweenness and low clustering coefficient. They were likely to be learners who participated in social interaction moderately actively. In the same study, Poquet and Dawson (2016) also identified information brokers among frequent contributors (who posted in at least three weeks) using the core-peripheral approach. An undirected weighted social network was
constructed for frequent contributors and showed a core-peripheral structure. At the core were a small group of learners who exchanged interaction with each other frequently and connected with more peers, including those in the periphery of the network. These core members were considered important information brokers between less active members.
A small number of MOOC studies examined changes that occurred over time in individual learners’ connectedness. Tawfik et al. (2017) found in a chemistry MOOC that degree and betweenness of the top 25 participants ranked by degree centrality showed little change over time. Yang et al. (2013) found in a literature MOOC that the few high centrality participants were mainly from the earliest cohort that participated in the forums; late starters often remained in the periphery, had trouble getting integrated into
discussions, and tended to make less contributions.
Strength of social connections
The strength of social connections between learners can be measured with weighted edges. Repeated interactions between two learners result in higher edge weight which indicates potentially stronger connections between the two. Yang, Wen, and Rosé (2014a) found that MOOC learners who developed a higher number of stronger social ties in the discussion forum were more likely to continue participating in the forum. Furthermore, Wise and Cui (2018a) examined a statistics MOOC and found forums participants in communities with strong inter-learner social connections tended to revisit discussion to offer help to others, after they had received help in these
discussions. Focusing on individual learners, each learner in the social network forms an ego network with others that are directly connected to the learner themselves (Scott, 2000). Average edge weight in the ego network indicates the overall strength of a learner’s social connections with those who have interacted directly with them.
Connection strength in ego networks have been found to impact many aspects of social interaction, such as willingness to collaborate and share resources with others
(Arnaboldi, Conti, Passarella, & Dunbar, 2017). Thus average edge weight in the ego network can be promising for understanding learner positions in MOOC discussions.