I don’t usually comment on articles in “closed” journals, but making an exception in this case. I hope you can find it in a library data base, or one of the authors uploads it to a public site or you can “rent ” it from Wiley for 48 hours for $6! The article:
Casquero, O., Ovelar, R., Romo, J., & Benito, M. (2015). Reviewing the differences in size, composition and structure between the personal networks of high- and low-performing students. British Journal of Educational Technology, 46(1), 16-31. http://dx.doi.org/10.1111/bjet.12110.
This is one of the few studies that use a quasi-experimental design to measure differences in network formation and structure between low and high achievers in two types of online learning contexts. The first context was based on traditional LMS (Moodle) activities and design with the the usual content display and threaded discussions. The second used a variety of tools including iGoogle, Google Groups and FriendFeed and an array of digital resource repositories such as Delicious, Flickr, YouTube, Scribd and SlideShare. The instructor and learning activities were the same in both contexts. Coincidently, this second model is similar to my own courses in which I use the LMS for grade management and some static content display. However, unlike the mix of tools used in this PLE, I use our in-house Elgg environment (Athabasca Landing) which enhances privacy and student control of data.
As has been found in very much studies of interaction in formal courses, the students who are most active (highest participation levels), score higher marks. This correlation is often used by researchers to justify their interaction interventions. However, as always correlation doesn’t imply causation. Involved, motivated students always both participate and score higher than those who don’t – no matter what learning activities are designed.
In this study social network analysis tools were used to measure the individual networks developed as evidenced by comments and contributions. As expected higher performing students had more highly developed, denser and more extensive social networks – again demonstrating motivation and participation. However, more interesting was that the PLE students interacted more and also built more expensive personal networks. The authors note:
in public spaces, such as open forums, all the individuals are equally exposed and equally positioned to access the information flow. As a result, the present study demonstrates that when public spaces based on indirect interactions are set up in online courses, students’ selection procedures for interaction are not focused on the individuals, but rather on those shared resources and the will to collaborate
Obviously, the information flow in Moodle forums can be rich, but the more extensive opportunities to contribute, and as importantly to browse and consume information produced by others, increases with heterogeneity and richness of sources of that flow.
One problem in this and other networking studies is the sample selection. As in far too many studies of online learning, in this study the 120 participants were all taking a course on Networking and Web 2.0. I have never seen data on how many online research studies use students studying some component of online learning as the subject matter. This is sort of like studying people’s reaction to smoking indoors, but only reporting the attitudes of smokers.
In any case this is an interesting study and provides further evidence for expanding the learning contexts beyond the confines of a teacher constructed LMS. Network growth, social capital accumulation, transparency, persistence and network literacy are all enhanced when these ‘connectivist’ learning outcomes are aimed for, and instantiated in a course that grows beyond the LMS.