Student Response

Research on student response analysis focuses on automating the processing and interpretation of student work, aiming to provide more efficient and insightful feedback for both students and educators. Current efforts leverage large language models (LLMs), such as GPT-3.5 and GPT-4, often incorporating techniques like chain-of-thought prompting and fine-tuning on specialized datasets to improve accuracy in tasks ranging from automated grading and feedback generation to identifying at-risk students. These advancements offer the potential to significantly improve the efficiency and effectiveness of educational assessment, providing more targeted feedback and supporting educators in adapting their teaching practices.