Get It Scored Using AutoSAS -- An Automated System for Scoring Short Answers
This system addresses the problem of efficiently and accurately grading short answers for millions of candidates in MOOCs, which is intractable for human graders. It offers a strong specific gain for educational technology platforms.
The paper introduces AutoSAS, an automated system for scoring short answers in online exams. AutoSAS achieves state-of-the-art performance on the ASAP-SAS dataset, outperforming previous methods by over 8% in some question prompts as measured by Quadratic Weighted Kappa (QWK), demonstrating human-comparable performance.
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.