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

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Title: Automated Identification of Logical Errors in Programs: Advancing Scalable Analysis of Student Misconceptions
Language: English
Authors: Muntasir Hoq, Ananya Rao, Reisha Jaishankar, Krish Piryani, Nithya Janapati, Jessica Vandenberg, Bradford Mott, Narges Norouzi, James Lester, Bita Akram
Source: International Educational Data Mining Society. 2025.
Availability: International Educational Data Mining Society. e-mail: admin@educationaldatamining.org; Web site: https://educationaldatamining.org/conferences/
Peer Reviewed: Y
Page Count: 14
Publication Date: 2025
Sponsoring Agency: National Science Foundation (NSF), Division of Undergraduate Education (DUE)
Contract Number: 2236195
2331965
Document Type: Speeches/Meeting Papers
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Computer Science Education, Programming, Misconceptions, Error Patterns, Coding, College Students, Grades (Scholastic), Prediction
Abstract: 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. [For the complete proceedings, see ED675583.]
Abstractor: As Provided
Entry Date: 2025
Accession Number: ED675600
Database: ERIC
FullText Text:
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  Data: 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. [For the complete proceedings, see ED675583.]
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