As part of my doctoral process, I have to review a lot of literature. A LOT. To motivate me to keep track of the resources as I read them, I am going to try to post summaries of the pertinent articles here. My hope is that I will get in the habit of writing and summarizing so that the literature review portion of my dissertation is easier to write. We’ll see if that works, and if I can keep up with the summaries!
Vercellone-Smith, P., Jablokow, K., & Friedel, C. (2012). Characterizing communication networks in a web-based classroom: Cognitive styles and linguistic behavior of self-organizing groups in online discussions. Computers & Education, 59(2), 222–235. doi: http://dx.doi.org/10.1016/j.compedu.2012.01.006
In this article, the researchers use social network analysis to investigate whether cognitive style impacts online discussion participation. Specifically, they investigated three aspects: participation (frequency and duration), patterns of information exchange (who students communicate with), and formation of communication hierarchies (influence in the classroom network). The study had four research questions:
- Do students preferentially communicate with other students who have similar cognitive styles?
- Does cognitive style impact a student’s level of online participation (e.g., frequency and/or length of communications)?
- Do the cognitive style preferences of the most highly engaged students in a communication network (i.e., the core) differ from those who are more loosely connected to the network (i.e., the periphery)?
- Do students in the core of the network exhibit different linguistic behaviors from those in the periphery?
To assess cognitive style, the researchers used Kirton’s Adaption Innovation Theory and the KAI® instrument. The instrument was administered at the beginning of the course to 21 students. The KAI scores were varied and typical of the results obtained with the tool.
Then, each of the 1131 messages posted to 19 online discussion forums were analyzed. Directed patterns of communication were recorded in a forum adjacency matrix to show which students replied to each other, then aggregated into a course adjacency matrix. In degree and Out degree were used to determine social hierarchy. One student with an in degree of 2 was socially isolated from the rest of the students. No correlation was found between cognitive style and either in-degree or out-degree centrality.
To determine whether cliques (groups of 2 or more participants who are more closely linked with one another than others in the network) had formed based on cognitive style, the researchers used UCINET to find dyads (2-member cliques) and triads (3-member cliques). No correlation was found between cognitive style and the number of cliques a student belonged to, but cognitive gap analysis across cliques showed that the majority were heterogeneous for adaptation-innovation. This is interesting because students self-organized into cliques that have the potential for more disagreement and tension. One explanation is that the online environment may mask these differences and encourage heterogeneous pairings more than a face-to-face environment.
The students also demonstrated a moderate-to-strong core formation, with 8 students in the core and 13 in the periphery. Further analysis showed that the mean KAI score for the core members was significantly more Adaptive than those in the periphery. The researchers speculate that this occurred because Adaptive students tend to desire more connection and control. Core members contributed 33% more text than peripheral students and used more positive emotion words in their posts.
This study is very interesting the social network analysis model is more developed here than in other studies. The sample size was small, so extending a similar approach to a massive online course would provide more robust measures.