clustering is putting similar things near similar things. Tree edit distance is quite a natural measure of distance for tree like things like programs.
You can't avoid some warping when putting high dimensional manifolds on a low dimensional one. You can see a lot of their data does cluster properly but their are some long range red arcs (in the embedding space) which are side effects of warping (they are near in data space).
You can see a cluster of green which is clearly of interest ... why did so many students get the wrong answer in the same way?
I see lots of value in that picture.
Well we have a lot of ideas! One thing that we did, for example, was to apply clustering to discover the ``typical'' approaches to this problem. This allowed us to discover common failure modes in the class, but also gave us a way to find multiple correct approaches to the same problem. Stay tuned for more results from the codewebs team!