  In the era of MOOCs, online exams are taken by millions of candidates, where
scoring short answers is an integral part. It becomes intractable to evaluate
them by human graders. Thus, a generic automated system capable of grading
these responses should be designed and deployed. In this paper, we present a
fast, 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 can
learn to grade that prompt successfully. This paper further lays down the
features such as lexical diversity, Word2Vec, prompt, and content overlap that
plays a pivotal role in building our proposed model. We also present a
methodology for indicating the factors responsible for scoring an answer. The
trained model is evaluated on an extensively used public dataset, namely
Automated Student Assessment Prize Short Answer Scoring (ASAP-SAS). AutoSAS
shows state-of-the-art performance and achieves better results by over 8% in
some of the question prompts as measured by Quadratic Weighted Kappa (QWK),
showing performance comparable to humans.
