Using GPT-4 to Augment Unbalanced Data for Automatic Scoring

Understand

Machine learning-based automatic scoring faces challenges with unbalanced student responses across scoring categories.

• To address this, we introduce a novel text data augmentation framework leveraging GPT-4, a generative large language model, specifically tailored for unbalanced datasets in automatic scoring.
• Our experimental dataset comprised student written responses to four science items.
• We crafted prompts for GPT-4 to generate responses, especially for minority scoring classes, enhancing the data set.