ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)

Palak Goenka, Mehak Piplani, Ramit Sawhney, Puneet Mathur, Rajiv Ratn Shah

AAAI 2020 pp. 13797-13798

doi:10.1609/AAAI.V34I10.7170 /aaai/2020/goenka2020aaai-esas/

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.

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Text

Goenka et al. "ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7170

Markdown

[Goenka et al. "ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/goenka2020aaai-esas/) doi:10.1609/AAAI.V34I10.7170

BibTeX

@inproceedings{goenka2020aaai-esas,
  title     = {{ESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)}},
  author    = {Goenka, Palak and Piplani, Mehak and Sawhney, Ramit and Mathur, Puneet and Shah, Rajiv Ratn},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13797-13798},
  doi       = {10.1609/AAAI.V34I10.7170},
  url       = {https://mlanthology.org/aaai/2020/goenka2020aaai-esas/}
}