# McMining: Automated Discovery of Misconceptions in Student Code

arXiv: https://arxiv.org/abs/2510.08827 · HTML: https://mlsanity.com/paper/2510.08827/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 9 Oct 2025.

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

- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Can Language Models Employ the Socratic Method? Experiments with Code Debugging](https://mlsanity.com/paper/2310.03210/) (2023)
- [Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts](https://mlsanity.com/paper/2404.19336/) (2024)
- [Automated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions](https://mlsanity.com/paper/2505.10913/) (2025)

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

- [Reasoning Trajectories for Socratic Debugging of Student Code: From Misconceptions to Contradictions and Updated Beliefs](https://mlsanity.com/paper/2511.00371/) (2025)

## Abstract (arXiv)

When learning to code, students often develop misconceptions about various programming language concepts. These can not only lead to bugs or inefficient code, but also slow down the learning of related concepts. In this paper, we introduce McMining, the task of mining programming misconceptions from samples of code from a student. To enable the training and evaluation of McMining systems, we develop an extensible benchmark dataset of misconceptions together with a large set of code samples where these misconceptions are manifested. We then introduce two LLM-based McMiner approaches and through extensive evaluations show that models from the Gemini, Claude, and GPT families are effective at discovering misconceptions in student code.