Automatic Feedback Generation on K-12 Students' Data Science Education by Prompting Cloud-based Large Language Models
Sze Ching Evelyn Fung, M.F. Wong, Chee Wei Tan
Abstract
Since data science is traditionally an advanced field taught at the college or university level, introducing its concepts to K-12 students can present unique learning challenges. As educational environments increasingly adopt data science curricula for K-12 students, the need for scalable, personalized teaching tools becomes critical. While the integration of large language models (LLMs) in educational environments offers significant potential for scalability and automation, it is important to note that the generated language output may not always be highly suitable for K-12 students. In this paper, we introduce the DSRAG, a novel educational automatic feedback generation framework that leverages Retrieval-Augmented Generation (RAG) and cloud-based LLMs to provide automated and personalized feedback for K-12 students engaged in data science education. DSRAG employs Langchain question-answering and RAG systems to manage educational content and generate feedback on the top of GPT-4. We also demonstrate the framework's capability to simplify complex concepts and align its responses to be pedagogically appropriate and understandable for K-12 students.