Improving Automated Feedback Systems for Tutor Training in Low-Resource Scenarios through Data Augmentation

TL;DR

This paper presents a data augmentation method using GPT-4o to generate synthetic training data, significantly improving automated feedback systems for tutor training in low-resource settings.

Contribution

It introduces a novel approach of leveraging GPT-4o for synthetic data generation to enhance tutor feedback models with limited labeled data.

Findings

Synthetic data improves praise type identification accuracy.

Augmentation enhances model performance in low-resource scenarios.

Method reduces reliance on extensive manual labeling.

Abstract

Tutoring is an effective instructional method for enhancing student learning, yet its success relies on the skill and experience of the tutors. This reliance presents challenges for the widespread implementation of tutoring, particularly in training novice tutors. To support tutor training programs, real-time automated feedback systems are essential for efficiently training large numbers of tutors. Lin et al.'s previous study employed Generative Pre-Trained Transformers (GPT) for sequence labeling to identify desirable and undesirable praise components in a tutor training dataset, providing explanatory feedback. However, this approach requires a significant amount of labeled data for fine-tuning, which is both labor-intensive and dependent on expert input. To address the challenges associated with extensive data labeling, the current study explores the use of prompting more advanced GPT models like GPT-4o to generate synthetic datasets for augmenting labeled response data, followed by fine-tuning a GPT-3.5 model. Our results demonstrate that our data augmentation approach generalizes effectively to identify other types of praise, compared to the same model fine-tuned without augmentation. These findings suggest that for data-intensive tasks, synthetic data generated through GPT model prompting can substantially enhance fine-tuned model performance in low-resource scenarios.