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In the era of MOOCs, online exams are taken by millions of candidates, wherescoring short answers is an integral part. It becomes intractable to evaluatethem by human graders. Thus, a generic automated system capable of gradingthese responses should be designed and deployed. In this paper, we present afast, scalable, and accurate approach towards automated Short Answer Scoring(SAS). We propose and explain the design and development of a system for SAS,namely AutoSAS. Given a question along with its graded samples, AutoSAS canlearn to grade that prompt successfully. This paper further lays down thefeatures such as lexical diversity, Word2Vec, prompt, and content overlap thatplays a pivotal role in building our proposed model. We also present amethodology for indicating the factors responsible for scoring an answer. Thetrained model is evaluated on an extensively used public dataset, namelyAutomated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSASshows state-of-the-art performance and achieves better results by over 8% insome of the question prompts as measured by Quadratic Weighted Kappa (QWK),showing performance comparable to humans.

Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers