# Automatic Short Answer Grading and Feedback Using Text Mining Methods

arXiv: https://arxiv.org/abs/1807.10543 · HTML: https://mlsanity.com/paper/1807.10543/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2018.

## Built on by (most cited first, 11 of 11)

- [Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers](https://mlsanity.com/paper/2204.03503/) (2022)
- [A LLM-Powered Automatic Grading Framework with Human-Level Guidelines Optimization](https://mlsanity.com/paper/2410.02165/) (2024)
- [Towards Trustworthy AutoGrading of Short, Multi-lingual, Multi-type Answers](https://mlsanity.com/paper/2201.03425/) (2022)
- [Investigating Automatic Scoring and Feedback using Large Language Models](https://mlsanity.com/paper/2405.00602/) (2024)
- [Disentangling Learning from Judgment: Representation Learning for Open Response Analytics](https://mlsanity.com/paper/2512.23941/) (2025)
- [SteLLA: A Structured Grading System Using LLMs with RAG](https://mlsanity.com/paper/2501.09092/) (2025)
- [An overview of artificial intelligence in computer-assisted language learning](https://mlsanity.com/paper/2505.02032/) (2025)
- [MAGIC: Multi-Agent Argumentation and Grammar Integrated Critiquer](https://mlsanity.com/paper/2506.13037/) (2025)
- [Finetuning Transformer Models to Build ASAG System](https://mlsanity.com/paper/2109.12300/) (2021)
- [Exploring the Relationship Between Feature Attribution Methods and Model Performance](https://mlsanity.com/paper/2405.13957/) (2024)
- [Automatic essay scoring: leveraging Jaccard coefficient and Cosine similaritywith n-gram variation in vector space model approach](https://mlsanity.com/paper/2510.15311/) (2025)

## Abstract (arXiv)

Automatic grading is not a new approach but the need to adapt the latest technology to automatic grading has become very important. As the technology has rapidly became more powerful on scoring exams and essays, especially from the 1990s onwards, partially or wholly automated grading systems using computational methods have evolved and have become a major area of research. In particular, the demand of scoring of natural language responses has created a need for tools that can be applied to automatically grade these responses. In this paper, we focus on the concept of automatic grading of short answer questions such as are typical in the UK GCSE system, and providing useful feedback on their answers to students. We present experimental results on a dataset provided from the introductory computer science class in the University of North Texas. We first apply standard data mining techniques to the corpus of student answers for the purpose of measuring similarity between the student answers and the model answer. This is based on the number of common words. We then evaluate the relation between these similarities and marks awarded by scorers. We then consider an approach that groups student answers into clusters. Each cluster would be awarded the same mark, and the same feedback given to each answer in a cluster. In this manner, we demonstrate that clusters indicate the groups of students who are awarded the same or the similar scores. Words in each cluster are compared to show that clusters are constructed based on how many and which words of the model answer have been used. The main novelty in this paper is that we design a model to predict marks based on the similarities between the student answers and the model answer.