ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract) - Goenka, Palak; Piplani, Mehak; Sawhney, Ramit; Mathur, Puneet; Shah, Rajiv Ratn (Proceedings of the AAAI Conference on Artificial Intelligence, 2020)
Motivated by the mandate to design and deploy a practical, real-world educational tool for grading, we extensively explore linguistic patterns for Short Answer Scoring (SAS) as well as authorship feedback. We approach the SAS task via a multipronged approach that employs linguistic context features for capturing domain-specific knowledge while emphasizing on domain agnostic grading and detailed feedback via an ensemble of explainable statistical models. Our methodology quantitatively supersedes multiple automatic short answer scoring systems.