A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science
<|reference_start|>A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science: This paper explores the use of large language models (LLMs) to score and explain short-answer assessments in K-12 science. While existing methods can score more structured math and computer science assessments, they often do not provide explanations for the scores. Our study focuses on employing GPT-4 for automated assessment in middle school Earth Science, combining few-shot and active learning with chain-of-thought reasoning. Using a human-in-the-loop approach, we successfully score and provide meaningful explanations for formative assessment responses. A systematic analysis of our method's pros and cons sheds light on the potential for human-in-the-loop techniques to enhance automated grading for open-ended science assessments.<|reference_end|>
@article{cohn2024a,
    title={A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students'
  Formative Assessment Responses in Science},
    author={Clayton Cohn, Nicole Hutchins, Tuan Le, Gautam Biswas},
    journal={arXiv preprint arXiv:2403.14565},
    year={2024},
    doi={10.1609/aaai.v38i21.30364},
    archivePrefix={arXiv},
    eprint={2403.14565},
    primaryClass={cs.CL}
}