Learning Code-Edit Embedding to Model Student Debugging Behavior
Project Overview
The document explores the application of generative AI in education, focusing on enhancing feedback mechanisms in computer science, particularly for programming assignments. It addresses the existing challenges educators face in providing effective and timely feedback, which is crucial for student learning. To tackle these issues, it introduces an innovative model leveraging encoder-decoder techniques to analyze student debugging behavior by learning from code-edit embeddings derived from previous submissions. This approach enables the generation of personalized next-step code suggestions, thereby guiding students more effectively in their learning process. Additionally, the model identifies and analyzes common debugging errors, which helps educators understand prevalent challenges faced by students. The ultimate goal is to improve learning outcomes through the provision of tailored and immediate feedback, demonstrating the potential of generative AI to transform educational practices and support student success in programming.
Key Applications
Encoder-decoder-based model for learning code-edit embeddings
Context: Computer science education for students in programming courses
Implementation: Utilizes historical student code submissions to generate personalized feedback and suggestions
Outcomes: Improves personalized next-step code suggestions and helps identify common debugging patterns
Challenges: May produce minor syntactical errors; risk of reducing engagement if feedback is too direct
Implementation Barriers
Technical
Providing fully correct solutions can diminish student engagement and learning.
Proposed Solutions: Ensure generated suggestions focus on incremental improvements rather than complete solutions.
Pedagogical
Feedback is often delayed or non-personalized, making it difficult for students to act on it promptly.
Proposed Solutions: Implement tools that provide real-time, personalized feedback based on student submissions.
Project Team
Hasnain Heickal
Researcher
Andrew Lan
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Hasnain Heickal, Andrew Lan
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai