# Automated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions

arXiv: https://arxiv.org/abs/2505.10913 · HTML: https://mlsanity.com/paper/2505.10913/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 70 references that are in the map.
- First posted on arXiv 16 May 2025.

## Builds on (most cited first, 12 of 12)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Can Large Language Models Be an Alternative to Human Evaluations?](https://mlsanity.com/paper/2305.01937/) (2023)
- [Regularizing Neural Networks by Penalizing Confident Output Distributions](https://mlsanity.com/paper/1701.06548/) (2017)
- [Deep Knowledge Tracing](https://mlsanity.com/paper/1506.05908/) (2015)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [code2vec: Learning Distributed Representations of Code](https://mlsanity.com/paper/1803.09473/) (2018)
- [EdNet: A Large-Scale Hierarchical Dataset in Education](https://mlsanity.com/paper/1912.03072/) (2019)
- [Code-DKT: A Code-based Knowledge Tracing Model for Programming Tasks](https://mlsanity.com/paper/2206.03545/) (2022)
- [Toward Semi-Automatic Misconception Discovery Using Code Embeddings](https://mlsanity.com/paper/2103.04448/) (2021)
- [Improving LLM Classification of Logical Errors by Integrating Error Relationship into Prompts](https://mlsanity.com/paper/2404.19336/) (2024)
- [Attentive Q-Matrix Learning for Knowledge Tracing](https://mlsanity.com/paper/2304.08168/) (2023)
- [Temporal and Between-Group Variability in College Dropout Prediction](https://mlsanity.com/paper/2401.06498/) (2024)

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

- [Pattern-based Knowledge Component Extraction from Student Code Using Representation Learning](https://mlsanity.com/paper/2508.09281/) (2025)
- [Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components](https://mlsanity.com/paper/2604.24758/) (2026)
- [McMining: Automated Discovery of Misconceptions in Student Code](https://mlsanity.com/paper/2510.08827/) (2025)
- [An Explainable AI Assistant for Introductory Programming Education: Improving Feedback Reliability with Instructor-AI Collaboration](https://mlsanity.com/paper/2606.12425/) (2026)
- [Automated Recommendation of Programming Learning Content Using Pattern-based Knowledge Components](https://mlsanity.com/paper/2607.05409/) (2026)

## Abstract (arXiv)

In Computer Science (CS) education, understanding factors contributing to students' programming difficulties is crucial for effective learning support. By identifying specific issues students face, educators can provide targeted assistance to help them overcome obstacles and improve learning outcomes. While identifying sources of struggle, such as misconceptions, in real-time can be challenging in current educational practices, analyzing logical errors in students' code can offer valuable insights. This paper presents a scalable framework for automatically detecting logical errors in students' programming solutions. Our framework is based on an explainable Abstract Syntax Tree (AST) embedding model, the Subtree-based Attention Neural Network (SANN), that identifies the structural components of programs containing logical errors. We conducted a series of experiments to evaluate its effectiveness, and the results suggest that our framework can accurately capture students' logical errors and, more importantly, provide us with deeper insights into their learning processes, offering a valuable tool for enhancing programming education.