Exploring Data Augmentation and Resampling Strategies for Transformer-Based Models to Address Class Imbalance in AI Scoring of Scientific Explanations in NGSS Classroom

This study explores data augmentation strategies to enhance transformer-based models for automated scoring of student scientific explanations, specifically addressing class imbalance. It evaluates methods like GPT-4 generated responses, EASE, and ALP against a SciBERT baseline, using a dataset of 1,466 high school responses.