Open AccessESAS: Towards Practical and Explainable Short Answer Scoring (Student Abstract)
Author(s) -
Palak Goenka,
Mehak Piplani,
Ramit Sawhney,
Puneet Mathur,
Rajiv Ratn Shah
Publication year - 2020
Publication title -
proceedings of the aaai conference on artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2374-3468
pISSN - 2159-5399
DOI - 10.1609/aaai.v34i10.7170
Subject(s) - grading (engineering) , computer science , natural language processing , artificial intelligence , task (project management) , domain (mathematical analysis) , mandate , domain specificity , machine learning , data science , psychology , engineering , mathematics , mathematical analysis , civil engineering , cognition , systems engineering , neuroscience , law , political science
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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