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Greetings -- I'm new to social network analysis, and would appreciate your recommendations wrto a question regarding affinities between general education courses at my institution. Given the relative complexity of our current GE requirements, my institution is considering a simple distribution requirement whereby students would select say 2-3 approved courses from each of our three divisions: mathematical and natural sciences, social sciences, and arts and humanities. The assumption here is that there is relative homogeneity among GE courses offered within each of these three divisions, coupled with sufficient distinctiveness between the divisions.
I'd like to empirically test this assumption. We have on order 50 GE courses: I could imagine asking the instructors for each of these courses to name say three other courses on this list that provide, in their estimate, the most similar educational experience to students. (Variants could include some ratings measure, naming courses that seem to least resemble their own, etc.) I'd imagine that this information would allow us to determine clusters, bridge courses, etc. There could be several outcomes, e.g. a visualization of our GE course space, but what would be of most utility would be potentially different ways of defining the substrate upon which we enact the distribution requirement, as some of us are justifiably weary of this three-box division of knowledge.
I've not done social network analysis before, and would appreciate your input in terms of both recommended applications and procedures, as well as what sort of data ideally to collect from instructors. I run a Mac, btw (latest OS; Java apps ok).
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