使用大型语言模型评估导师在应对学生数学错误时的表现
Using Large Language Models to Assess Tutors' Performance in Reacting to Students Making Math Errors
AI总结：
本研究探讨了利用生成式AI（GPT-3.5-Turbo和GPT-4）评估真实导师应对学生数学错误表现的能力，发现两者在评估应对标准上表现熟练，但在识别学生错误实例时存在局限，尤其是GPT-4易过度识别错误。
摘要：
研究表明，导师在处理低效能学生所犯的数学错误时应采取策略性方法。导师不应直接将注意力引向错误，而应引导学生自主发现并纠正错误。尽管导师培训课程已引入这一教学技能，但对导师应用该策略的情况进行人工评估既繁重又耗时。大型语言模型（LLMs）有望在导师实际辅导过程中提供实时评估，但其在该情境下的准确性尚不清楚。在本研究中，我们调查了生成式AI评估真实导师在应对学生数学错误时表现的能力。通过分析50段真实辅导对话，我们发现GPT-3.5-Turbo和GPT-4在评估应对学生犯错相关标准方面均表现出熟练度。然而，两个模型在识别学生犯错的具体实例时均存在局限性。值得注意的是，GPT-4倾向于过度识别学生犯错的实例，经常将学生的不确定性归因为错误，或推断出潜在错误，而人工评估员并未如此。未来的工作将侧重于通过评估更大的对话数据集和评估学习迁移来增强泛化能力。具体而言，我们将分析导师在完成关于这一关键辅导技能的课程前后，在真实场景中应对学生数学错误的表现。
英文摘要：
Research suggests that tutors should adopt a strategic approach when addressing math errors made by low-efficacy students. Rather than drawing direct attention to the error, tutors should guide the students to identify and correct their mistakes on their own. While tutor lessons have introduced this pedagogical skill, human evaluation of tutors applying this strategy is arduous and time-consuming. Large language models (LLMs) show promise in providing real-time assessment to tutors during their actual tutoring sessions, yet little is known regarding their accuracy in this context. In this study, we investigate the capacity of generative AI to evaluate real-life tutors' performance in responding to students making math errors. By analyzing 50 real-life tutoring dialogues, we find both GPT-3.5-Turbo and GPT-4 demonstrate proficiency in assessing the criteria related to reacting to students making errors. However, both models exhibit limitations in recognizing instances where the student made an error. Notably, GPT-4 tends to overidentify instances of students making errors, often attributing student uncertainty or inferring potential errors where human evaluators did not. Future work will focus on enhancing generalizability by assessing a larger dataset of dialogues and evaluating learning transfer. Specifically, we will analyze the performance of tutors in real-life scenarios when responding to students' math errors before and after lesson completion on this crucial tutoring skill.
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