Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education
A new research paper explores the use of Large Language Models (LLMs) for automatically assessing student self-explanations in programming education. The study compares LLM-based scoring methods against traditional semantic similarity techniques, aiming to determine the most effective approach for evaluating student-generated content. This research addresses the challenge of accurately judging the correctness of student explanations, a crucial component for enhancing learning through worked examples. AI
IMPACT This research could lead to more efficient and scalable methods for evaluating student understanding in programming courses.