# Automated assessment of non-native learner essays: Investigating the role of linguistic features

arXiv: https://arxiv.org/abs/1612.00729 · HTML: https://mlsanity.com/paper/1612.00729/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2016.

## Built on by (most cited first, 12 of 12)

- [Exploring LLM Prompting Strategies for Joint Essay Scoring and Feedback Generation](https://mlsanity.com/paper/2404.15845/) (2024)
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- [Text mining in education](https://mlsanity.com/paper/2403.00769/) (2024)
- [Essay-BR: a Brazilian Corpus of Essays](https://mlsanity.com/paper/2105.09081/) (2021)
- [H-AES: Towards Automated Essay Scoring for Hindi](https://mlsanity.com/paper/2302.14635/) (2023)
- [Automatic Essay Multi-dimensional Scoring with Fine-tuning and Multiple Regression](https://mlsanity.com/paper/2406.01198/) (2024)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [ChatPRCS: A Personalized Support System for English Reading Comprehension based on ChatGPT](https://mlsanity.com/paper/2309.12808/) (2023)
- [Automated Essay Scoring Using Grammatical Variety and Errors with Multi-Task Learning and Item Response Theory](https://mlsanity.com/paper/2406.08817/) (2024)
- [The Anatomy of Speech Persuasion: Linguistic Shifts in LLM-Modified Speeches](https://mlsanity.com/paper/2506.18621/) (2025)

## Abstract (arXiv)

Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in real life applications today. However, not much is known about which specific linguistic features are useful for prediction and how much of this is consistent across datasets. This article addresses that by exploring the role of various linguistic features in automatic essay scoring using two publicly available datasets of non-native English essays written in test taking scenarios. The linguistic properties are modeled by encoding lexical, syntactic, discourse and error types of learner language in the feature set. Predictive models are then developed using these features on both datasets and the most predictive features are compared. While the results show that the feature set used results in good predictive models with both datasets, the question "what are the most predictive features?" has a different answer for each dataset.