ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)
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.
How to Cite
Goenka, P., Piplani, M., Sawhney, R., Mathur, P., & Shah, R. R. (2020). ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 34(10), 13797-13798. https://doi.org/10.1609/aaai.v34i10.7170
Student Abstract Track