结合大型语言模型与辅导系统智能：照护者家庭作业支持案例研究
Combining Large Language Models with Tutoring System Intelligence: A Case Study in Caregiver Homework Support
AI总结：
本研究结合大型语言模型与辅导系统智能，开发对话式支持系统，为照护者提供家庭作业教学建议，经中学照护者评估，有效促进内容支持和学生元认知。
摘要：
照护者（即父母及儿童关爱社区的成员）在学习分析中是被低估的利益相关者。尽管照护者参与能够提升学生的学业成果，但许多障碍阻碍了参与，最显著的是对现代学校课程的知识缺口。学习分析中一个新兴的关注主题是混合辅导，包括教学支持和动机支持。照护者在家庭作业中扮演类似角色，但尚不清楚学习分析如何支持他们。我们过去与照护者的工作表明，对话式支持是为照护者提供有效支持学生学习所需指导的一种有前景的方法。我们开发了一个系统，通过大型语言模型（LLM）生成的对话建议为照护者提供教学支持。针对LLM已知的教学局限性，我们在使用开源Llama 3 LLM进行提示工程实验的同时，利用辅导系统的教学智能。该LLM为照护者通过聊天支持孩子数学练习生成消息建议。少样本提示和将辅导系统的实时问题解决上下文与辅导实践示例相结合，产生了理想的消息建议。这些建议由十位中学照护者评估，他们重视促进内容层面支持和通过自我解释增强学生元认知的建议。我们贡献了关于如何最佳地将辅导系统与LLM融合，通过对话辅助支持混合辅导环境，促进照护者有效参与辅导系统的见解。
英文摘要：
Caregivers (i.e., parents and members of a child's caring community) are underappreciated stakeholders in learning analytics. Although caregiver involvement can enhance student academic outcomes, many obstacles hinder involvement, most notably knowledge gaps with respect to modern school curricula. An emerging topic of interest in learning analytics is hybrid tutoring, which includes instructional and motivational support. Caregivers assert similar roles in homework, yet it is unknown how learning analytics can support them. Our past work with caregivers suggested that conversational support is a promising method of providing caregivers with the guidance needed to effectively support student learning. We developed a system that provides instructional support to caregivers through conversational recommendations generated by a Large Language Model (LLM). Addressing known instructional limitations of LLMs, we use instructional intelligence from tutoring systems while conducting prompt engineering experiments with the open-source Llama 3 LLM. This LLM generated message recommendations for caregivers supporting their child's math practice via chat. Few-shot prompting and combining real-time problem-solving context from tutoring systems with examples of tutoring practices yielded desirable message recommendations. These recommendations were evaluated with ten middle school caregivers, who valued recommendations facilitating content-level support and student metacognition through self-explanation. We contribute insights into how tutoring systems can best be merged with LLMs to support hybrid tutoring settings through conversational assistance, facilitating effective caregiver involvement in tutoring systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
