Many thanks to George Siemens for pointing out SNAPP: Social Networks Adapting Pedagogical Practice. This is something I’ve been urging various LMS communities to build for seven or eight years (since I was involved with dotLRN). SNAPP extracts information from the discussion boards in your LMS course and provides social graphs that help you to visualize interactivity.
It works with Blackboard, ANGEL, Desire2Learn, and Moodle (but not Sakai). The mechanism by which it works isn’t entirely clear from the project site, but it looks like you use a bookmarklet to call a web service hosted elsewhere while browsing the discussion forum you want to analyze.
There’s quite a bit that you can do with a tool like this. Here are some applications that the SNAPP site suggests:
- identify disconnected (at risk) students;
- identify key information brokers within your class;
- identify potentially high and low performing students so you can plan interventions before you even mark their work;
- indicate the extent to which a learning community is developing in your class;
- provide you with a “before and after” snapshot of what kinds of interactions happened before and after you intervened/changed your learning activity design (useful to see what effect your changes have had on student interactions and for demonstrating reflective teaching practice e.g. through a teaching portfolio)
- allow your students to benchmark their performance without the need for marking.
But I think this is just the tip of the iceberg. The reason I first reached for social network analysis way back when was to settle an age-old debate about which discussion board interface is “best.” My contention has always been that different discussion board UIs foster different kinds of conversation. If you want a Q&A-style conversation where people post atomic questions and get a string of direct answers, use a threaded interface. If you want a wide-ranging conversation in which you get a lot of student-to-student interactions, use a flat interface. A social network analysis tool like this one, particularly if it could aggregate data across multiple discussion boards and multiple courses, could help to answer this question as well as others, e.g., what are the differences in student interactions when you use course blogs with comments versus a discussion board?
We can do yet more with social network analysis if we can shake off the shackles of current-generation LMS architecture and thinking. For example, today’s LMS encourages us to think of every course as atomic and in its own box. We might do cross-course analysis to see, say, trends in terms of which LMS tools are getting used the most, but we don’t look at this wealth of transactional data to analyze students’ cross-course behavior. To the extent that schools do student cross-course performance analytics at all, they generally look at the longitudinal data in their SIS. But we could be investigating, for example, the degree to which students’ educational social networks extend beyond an individual class. Are there students that “travel” together across courses? If so, do they tend to interact with each other more than mean student-to-student interaction within particular courses? If so, does that impact their overall performance relative to their peers? If so, can we take measures that encourage students to form these cohorts, and identify students who don’t participate in these cohorts as at-risk? And so on. Imagine embedding this sort of analysis into a student’s ePortfolio as well. We could begin to give students an opportunity to show not only what they’ve learned and how much they’ve improved but also how they have learned and improved.
We also can do more as LMSs get more sophisticated about content ownership, content re-use, and social connections. For example, one instructor re-using another instructor’s learning object can be graphed as a social connection. What can we learn about fostering a culture of re-use by graphing patterns of re-use? To what extent is content re-use driven by relatively anonymous search-type discovery methods, and to what extent is it a social activity?
There’s a good deal of social network analysis software out there, both open source and private source. I’d love to see LMS development communities tackle this challenge in a more wide-ranging way.