# Data-Mining Textual Responses to Uncover Misconception Patterns

arXiv: https://arxiv.org/abs/1703.08544 · HTML: https://mlsanity.com/paper/1703.08544/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2017.

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

- [Toward Semi-Automatic Misconception Discovery Using Code Embeddings](https://mlsanity.com/paper/2103.04448/) (2021)
- [Do LLMs Make Mistakes Like Students? Exploring Natural Alignment between Language Models and Human Error Patterns](https://mlsanity.com/paper/2502.15140/) (2025)
- [Learning Skill Equivalencies Across Platform Taxonomies](https://mlsanity.com/paper/2102.09377/) (2021)
- [MiRAGE: Misconception Detection with Retrieval-Guided Multi-Stage Reasoning and Ensemble Fusion](https://mlsanity.com/paper/2511.01182/) (2025)
- [What Don't You Understand? Using Large Language Models to Identify and Characterize Student Misconceptions About Challenging Topics](https://mlsanity.com/paper/2605.00294/) (2026)

## Abstract (arXiv)

An important, yet largely unstudied, problem in student data analysis is to detect misconceptions from students' responses to open-response questions. Misconception detection enables instructors to deliver more targeted feedback on the misconceptions exhibited by many students in their class, thus improving the quality of instruction. In this paper, we propose a new natural language processing-based framework to detect the common misconceptions among students' textual responses to short-answer questions. We propose a probabilistic model for students' textual responses involving misconceptions and experimentally validate it on a real-world student-response dataset. Experimental results show that our proposed framework excels at classifying whether a response exhibits one or more misconceptions. More importantly, it can also automatically detect the common misconceptions exhibited across responses from multiple students to multiple questions; this property is especially important at large scale, since instructors will no longer need to manually specify all possible misconceptions that students might exhibit.