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

TL;DR

AutoSAS is an automated short answer scoring system that leverages lexical, semantic, and prompt features to achieve human-level accuracy and scalability in grading student responses in online assessments.

Contribution

The paper introduces AutoSAS, a novel scalable system for automated short answer scoring that outperforms existing methods by over 8% in accuracy on a standard dataset.

Findings

AutoSAS achieves state-of-the-art performance on the ASAP-SAS dataset.

The system improves scoring accuracy by over 8% compared to previous methods.

Features like lexical diversity, Word2Vec, and content overlap are crucial for performance.

Abstract

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.