Assessing the Effectiveness of Large Language Models in Predicting Student Dropout Rates

Leveraging Large Language Models (LLMs) in educational analytics introduces promising and innovative approaches, particularly in predicting student dropout rates. One of these new approaches addresses the challenges of predicting student dropout rates. This study investigates the integration of advanced artificial intelligence (AI) tools, including OpenAI LLMs, HuggingFace embeddings, and FAISS vector databases, within a Few-Shot Learning framework. This study aims to identify subtle indicators of potential dropout risks by utilizing LLM’s contextual understanding and flexibility. Retrieval Augmented Generation (RAG) techniques were employed to improve the quality of contextual data provided to these models. Through extensive experiments with real-world data, we demonstrate that LLMs outperform traditional machine learning algorithms in predictive accuracy and provide valuable textual analyses of student data. This research highlights the potential of LLMs as powerful tools for dropout prediction in academic settings, contributing to ongoing efforts to reduce dropout rates.