# Directed Graph-alignment Approach for Identification of Gaps in Short Answers

arXiv: https://arxiv.org/abs/2504.04473 · HTML: https://mlsanity.com/paper/2504.04473/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 39 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 3 of 3)

- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Infusing Knowledge into the Textual Entailment Task Using Graph Convolutional Networks](https://mlsanity.com/paper/1911.02060/) (2019)
- [Joint Multi-Domain Learning for Automatic Short Answer Grading](https://mlsanity.com/paper/1902.09183/) (2019)

## Built on by (most cited first, 1 of 1)

- [How effective are VLMs in assisting humans in inferring the quality of mental models from Multimodal short answers?](https://mlsanity.com/paper/2603.00056/) (2026)

## Abstract (arXiv)

In this paper, we have presented a method for identifying missing items known as gaps in the student answers by comparing them against the corresponding model answer/reference answers, automatically. The gaps can be identified at word, phrase or sentence level. The identified gaps are useful in providing feedback to the students for formative assessment. The problem of gap identification has been modelled as an alignment of a pair of directed graphs representing a student answer and the corresponding model answer for a given question. To validate the proposed approach, the gap annotated student answers considering answers from three widely known datasets in the short answer grading domain, namely, University of North Texas (UNT), SciEntsBank, and Beetle have been developed and this gap annotated student answers' dataset is available at: https://github.com/sahuarchana7/gaps-answers-dataset. Evaluation metrics used in the traditional machine learning tasks have been adopted to evaluate the task of gap identification. Though performance of the proposed approach varies across the datasets and the types of the answers, overall the performance is observed to be promising.