# Algebra Error Classification with Large Language Models

arXiv: https://arxiv.org/abs/2305.06163 · HTML: https://mlsanity.com/paper/2305.06163/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 8 May 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education](https://mlsanity.com/paper/2106.07340/) (2021)
- [Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions](https://mlsanity.com/paper/1501.04346/) (2015)
- [Automatic Short Math Answer Grading via In-context Meta-learning](https://mlsanity.com/paper/2205.15219/) (2022)
- [Tree-Based Representation and Generation of Natural and Mathematical Language](https://mlsanity.com/paper/2302.07974/) (2023)
- [Math Operation Embeddings for Open-ended Solution Analysis and Feedback](https://mlsanity.com/paper/2104.12047/) (2021)

## Built on by (most cited first, 4 of 4)

- [Improving the Validity of Automatically Generated Feedback via Reinforcement Learning](https://mlsanity.com/paper/2403.01304/) (2024)
- [A Symbolic Framework for Evaluating Mathematical Reasoning and Generalisation with Transformers](https://mlsanity.com/paper/2305.12563/) (2023)
- [Interpreting Latent Student Knowledge Representations in Programming Assignments](https://mlsanity.com/paper/2405.08213/) (2024)
- [Can MLLMs Read Students' Minds? Unpacking Multimodal Error Analysis in Handwritten Math](https://mlsanity.com/paper/2603.24961/) (2026)

## Abstract (arXiv)

Automated feedback as students answer open-ended math questions has significant potential in improving learning outcomes at large scale. A key part of automated feedback systems is an error classification component, which identifies student errors and enables appropriate, predefined feedback to be deployed. Most existing approaches to error classification use a rule-based method, which has limited capacity to generalize. Existing data-driven methods avoid these limitations but specifically require mathematical expressions in student responses to be parsed into syntax trees. This requirement is itself a limitation, since student responses are not always syntactically valid and cannot be converted into trees. In this work, we introduce a flexible method for error classification using pre-trained large language models. We demonstrate that our method can outperform existing methods in algebra error classification, and is able to classify a larger set of student responses. Additionally, we analyze common classification errors made by our method and discuss limitations of automated error classification.