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.
Basu et al. REF present an approach to train a similarity metric between short answer responses to United States Citizenship Exam, which is then used to group the responses into clusters and subclusters for powergrading.
Powergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading
Transactions of the Association for Computational Linguistics
Transactions of the Association for Computational Linguistics
Computer Science