标题： 通过数据增强改进低资源场景下导师培训的自动化反馈系统 显示英文标题

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

摘要： 辅导是一种有效的教学方法，可以增强学生的学习，但其成功依赖于导师的技能和经验。 这种依赖性给辅导的广泛实施带来了挑战，尤其是在培训新手导师方面。 为了支持导师培训项目，实时自动反馈系统对于高效培训大量导师至关重要。 Lin 等人的先前研究使用了生成式预训练变换器（GPT）进行序列标注，以识别辅导培训数据集中有益和不良的表扬成分，并提供解释性反馈。 然而，这种方法需要大量的标记数据进行微调，这既费时又依赖专家输入。 为了解决大量数据标注带来的挑战，本研究探索了使用更先进的 GPT 模型，如 GPT-4o，通过提示生成合成数据集来扩充标记响应数据，随后对 GPT-3.5 模型进行微调。 我们的结果表明，与未进行数据增强的相同模型相比，我们的数据增强方法在识别其他类型的表扬方面具有良好的泛化能力。 这些发现表明，对于数据密集型任务，通过 GPT 模型提示生成的合成数据可以显著提高低资源场景下微调模型的性能。 显示英文摘要

摘要： 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.