# A School Student Essay Corpus for Analyzing Interactions of Argumentative Structure and Quality

arXiv: https://arxiv.org/abs/2404.02529 · HTML: https://mlsanity.com/paper/2404.02529/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 62 references that are in the map.
- First posted on arXiv 3 Apr 2024.

## Builds on (most cited first, 11 of 11)

- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [AdapterFusion: Non-Destructive Task Composition for Transfer Learning](https://mlsanity.com/paper/2005.00247/) (2020)
- [AdapterHub: A Framework for Adapting Transformers](https://mlsanity.com/paper/2007.07779/) (2020)
- [Parsing Argumentation Structures in Persuasive Essays](https://mlsanity.com/paper/1604.07370/) (2016)
- [Neural Automated Essay Scoring and Coherence Modeling for Adversarially Crafted Input](https://mlsanity.com/paper/1804.06898/) (2018)
- [Annotation and Classification of Sentence-level Revision Improvement](https://mlsanity.com/paper/1909.05309/) (2019)
- [Assessing the Sufficiency of Arguments through Conclusion Generation](https://mlsanity.com/paper/2110.13495/) (2021)
- [To Revise or Not to Revise: Learning to Detect Improvable Claims for Argumentative Writing Support](https://mlsanity.com/paper/2305.16799/) (2023)
- [A Corpus for Argumentative Writing Support in German](https://mlsanity.com/paper/2010.13674/) (2020)
- [Predicting the Quality of Revisions in Argumentative Writing](https://mlsanity.com/paper/2306.00667/) (2023)

## Built on by (most cited first, 5 of 5)

- [ArgInstruct: Specialized Instruction Fine-Tuning for Computational Argumentation](https://mlsanity.com/paper/2505.22076/) (2025)
- [Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method](https://mlsanity.com/paper/2505.12028/) (2025)
- [Toward LLM-Supported Automated Assessment of Critical Thinking Subskills](https://mlsanity.com/paper/2510.12915/) (2025)
- [Debating for Better Reasoning: An Unsupervised Multimodal Approach](https://mlsanity.com/paper/2505.14627/) (2025)
- [Evaluating Austrian A-Level German Essays with Large Language Models for Automated Essay Scoring](https://mlsanity.com/paper/2603.06066/) (2026)

## Abstract (arXiv)

Learning argumentative writing is challenging. Besides writing fundamentals such as syntax and grammar, learners must select and arrange argument components meaningfully to create high-quality essays. To support argumentative writing computationally, one step is to mine the argumentative structure. When combined with automatic essay scoring, interactions of the argumentative structure and quality scores can be exploited for comprehensive writing support. Although studies have shown the usefulness of using information about the argumentative structure for essay scoring, no argument mining corpus with ground-truth essay quality annotations has been published yet. Moreover, none of the existing corpora contain essays written by school students specifically. To fill this research gap, we present a German corpus of 1,320 essays from school students of two age groups. Each essay has been manually annotated for argumentative structure and quality on multiple levels of granularity. We propose baseline approaches to argument mining and essay scoring, and we analyze interactions between both tasks, thereby laying the ground for quality-oriented argumentative writing support.