Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading
We introduce a new approach to the machine-assisted grading of short answer questions. We follow past work in automated grading by first training a similarity metric between student responses, but then go on to use this metric to group responses into clusters and subclusters. The resulting groupings allow teachers to grade multiple responses with a single action, provide rich feedback to groups of similar answers, and discover modalities of misunderstanding among students; we refer to this amplification of grader effort as {``}powergrading.{''} We develop the means to further reduce teacher effort by automatically performing actions when an answer key is available. 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.
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