Fine-Tuning Large Language Models for Data Augmentation to Detect At-Risk Students in Online Learning Communities

Hongming Li, Anthony F Botelho

Abstract: We introduce a working approach that combines the method of fine-tuning large language models (LLMs) to create augmented data for the regression predictive models aimed at detecting at-risk students in online learning communities. This approach has the potential to leverage scarce data to improve urgency detection, and it can also present the role of artificial intelligence in enhancing the resilience of educational communities and ensuring timely interventions within online learning settings.