@article{basu-etal-2013-powergrading,
    title = "{P}owergrading: a Clustering Approach to Amplify Human Effort for Short Answer Grading",
    author = "Basu, Sumit  and
      Jacobs, Chuck  and
      Vanderwende, Lucy",
    editor = "Lin, Dekang  and
      Collins, Michael",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "1",
    year = "2013",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/Ingest-2025-COMPUTEL/Q13-1032/",
    doi = "10.1162/tacl_a_00236",
    pages = "391--402",
    abstract = "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 {\textquotedblleft}powergrading.{\textquotedblright} 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 {\textquotedblleft}budget{\textquotedblright} of human actions, both from our method and an LDA-based approach, on a test corpus of 10 questions answered by 698 respondents."
}