@misc{
 title = {The eras and trends of automatic short answer grading},
 type = {misc},
 year = {2015},
 source = {International Journal of Artificial Intelligence in Education},
 identifiers = {[object Object]},
 keywords = {Automatic grading,Natural language processing,Short answer},
 pages = {60-117},
 volume = {25},
 issue = {1},
 month = {1},
 publisher = {Springer New York LLC},
 day = {10},
 id = {2d74c1a1-2ba8-314c-b001-9cbde9a96969},
 created = {2020-02-05T01:31:46.139Z},
 accessed = {2020-02-04},
 file_attached = {true},
 profile_id = {66be748e-b1e3-36e1-95e1-5830d0ccc3ca},
 group_id = {ed1fa25d-c56b-3067-962d-9d08ff49394c},
 last_modified = {2020-02-05T01:32:00.840Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {c2955262-615e-4e71-ba11-ca23b3748d20},
 private_publication = {false},
 abstract = {Automatic short answer grading (ASAG) is the task of assessing short natural language responses to objective questions using computational methods. The active research in this field has increased enormously of late with over 80 papers fitting a definition of ASAG. However, the past efforts have generally been ad-hoc and non-comparable until recently, hence the need for a unified view of the whole field. The goal of this paper is to address this aim with a comprehensive review of ASAG research and systems according to history and components. Our historical analysis identifies 35 ASAG systems within 5 temporal themes that mark advancement in methodology or evaluation. In contrast, our component analysis reviews 6 common dimensions from preprocessing to effectiveness. A key conclusion is that an era of evaluation is the newest trend in ASAG research, which is paving the way for the consolidation of the field.},
 bibtype = {misc},
 author = {Burrows, Steven and Gurevych, Iryna and Stein, Benno}
}

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