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From Granular Revision Operations to Meaningful Revision Units: Evaluating LLMs for Revision Boundary Detection
Revision traces provide valuable evidence about students' writing processes, but their usefulness for learning analytics depends on how individual revisions are represented. Automated draft-comparison methods often produce granular edit operations that can fragment a single purposeful revision into
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