We show results in terms of grading progress with a small "budget" of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents.
instead of classifying individual answers as being right or wrong, we propose to automatically find groupings and subgroupings of similar answers from a large set of answers to the same question, and let teachers apply their expertise to mark the groups.