Marking: Visual Grading with Highlighting Errors and Annotating Missing Bits

This paper introduces 'Marking', an automated grading system that goes beyond binary scoring by highlighting correct/incorrect/irrelevant segments in student responses and identifying omissions from gold answers. The authors use NLI-based transformer models (BERT, RoBERTa) and create the BioMarking dataset with biology subject matter expert annotations to train and evaluate this fine-grained feedback system.

In this paper, we introduce"Marking", a novel grading task that enhances automated grading systems by performing an in-depth analysis of student responses and providing students with visual highlights. Unlike traditional systems that provide binary scores,"marking"identifies and categorizes segments of the student response as correct, incorrect, or irrelevant and detects omissions from gold answers. We introduce a new dataset meticulously curated by Subject Matter Experts specifically for this t

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