# Cognitively Aided Zero-Shot Automatic Essay Grading

arXiv: https://arxiv.org/abs/2102.11258 · HTML: https://mlsanity.com/paper/2102.11258/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 5 of 5)

- [Automated essay scoring with string kernels and word embeddings](https://mlsanity.com/paper/1804.07954/) (2018)
- [SkipFlow: Incorporating Neural Coherence Features for End-to-End Automatic Text Scoring](https://mlsanity.com/paper/1711.04981/) (2017)
- [Co-Attention Based Neural Network for Source-Dependent Essay Scoring](https://mlsanity.com/paper/1908.01993/) (2019)
- [Eyes are the Windows to the Soul: Predicting the Rating of Text Quality Using Gaze Behaviour](https://mlsanity.com/paper/1810.04839/) (2018)
- [Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour](https://mlsanity.com/paper/2005.12078/) (2020)

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

- [Empirical Study of Large Language Models as Automated Essay Scoring Tools in English Composition__Taking TOEFL Independent Writing Task for Example](https://mlsanity.com/paper/2401.03401/) (2024)

## Abstract (arXiv)

Automatic essay grading (AEG) is a process in which machines assign a grade to an essay written in response to a topic, called the prompt. Zero-shot AEG is when we train a system to grade essays written to a new prompt which was not present in our training data. In this paper, we describe a solution to the problem of zero-shot automatic essay grading, using cognitive information, in the form of gaze behaviour. Our experiments show that using gaze behaviour helps in improving the performance of AEG systems, especially when we provide a new essay written in response to a new prompt for scoring, by an average of almost 5 percentage points of QWK.