Gilad Ravid and Sheizaf Rafaeli’s new piece in FirstMonday, “Asynchronous Discussion Groups as Small World and Scale Free Networks“, analyzes a voluntary learning community that develops on a university’s LMS. These are all students who are (apparently) registered for on-campus web-enhanced courses with strictly voluntary web-enhanced components. Interestingly, the study analyzed networking for the entire group of online participants across course boundaries. So the results are for the network characteristics of the entire online student body, as opposed to the network characteristics of student participation in individual courses. Ravid and Rafaeli find that the community formed by the students appears to be both a Small World and a Scale Free network.
What does this mean? To understand what a “Small World” network is, just think of the clich矯f “six degrees of separation” (and to experience it, visit the Oracle of Bacon). It means you generally know somebody who knows somebody who knows the person that you want to know. You can have different degrees of separation, too; for example, six degrees of separation means that the target person (Kevin Bacon, or whoever) is six people away from you in the chain of acquaintances. Ravid and Rafaeli found that the students in their experiment were separated from each other an average of 4.67 degrees. Small World networks are significant because information tends to diffuse very quickly through them. So, for example, if students in one class learn of a new open access journal that would be of general value to anyone in their major, then students who are not in that class but who participate in the online network are likely to learn about the journal from their peers.
A Scale Free network is a particular kind of Small World network that relies on a small number of well-connected hubs to make most of the connections. On the internet, Yahoo! is a hub in the Scale Free network because, for example, the shortest path from my blog to, say, Slashdot, is likely to be my blog to Yahoo! to Slashdot. The shortest distance between, say, your blog and the Yokohama Tire company website is likely to be a similar path that passes through Yahoo!. Now, if the network nodes are human beings, then these people function as what Malcolm Gladwell calls “connectors” in his book The Tipping Point. These are the people who know seem to know everyone and hear all the hottest info first. Knowing who the connectors are in a social network means that you know who to tell if you want everybody to know something quickly.
So this study has some implications for knowledge diffusion across a college community. I’d love to see follow-up studies focusing on different sub-groups (e.g., within a major, within a class, etc.) and comparing those networks to the larger ones. Also, there’s no reason why a network analysis tool couldn’t or shouldn’t be built into every LMS. Think how valuable it would be for a department or even a professor to be able to see how knowledge is traveling within the student community. Even better, imagine letting the students see this data. They could even get credit toward a grade based on their ability to be good knowledge diffusers. This might go a long way toward eliminating the kind of “call and response” probem in many online class discussions, where the professor posts a question, each student answers, and the conversation goes no further. By rewarding students for participating in measurable knowledge diffusion (through discussion boards, blogs, or whatever), you directly reward them for building dialogues.
(Article found via Smart Mobs.)
