标题： 学生在基于大语言模型的学习环境中与人工智能代理进行协作学习的参与度：聚类分析 显示英文标题

标题： Student engagement in collaborative learning with AI agents in an LLM-empowered learning environment: A cluster analysis

摘要： 将大型语言模型（LLM）模型整合到教育实践中，通过适应不同学习者类型的多样化行为模式，促进了个性化学习。 本研究旨在探索一种新颖的互动环境中这些学习者类型，并对其独特特征和互动动态进行详细分析。 研究涉及来自中国一所大学的110名学生，他们在基于LLM的学习环境中与多个LLM代理进行互动，完成了六个模块的课程作业。 收集并分析了学生非认知特质、课程参与度以及与人工智能的互动模式数据。 使用层次聚类分析，将学生分为三个不同的群体：积极提问者、响应导航者和沉默倾听者。 随后应用认识论网络分析，进一步界定不同学习者类型之间的互动档案和认知参与度。 研究结果强调了不同学习者类型如何与人机交互学习进行互动，并为自适应教育系统的设计提供了实际意义。 显示英文摘要

摘要： Integrating LLM models into educational practice fosters personalized learning by accommodating the diverse behavioral patterns of different learner types. This study aims to explore these learner types within a novel interactive setting, providing a detailed analysis of their distinctive characteristics and interaction dynamics. The research involved 110 students from a university in China, who engaged with multiple LLM agents in an LLM-empowered learning environment, completing coursework across six modules. Data on the students' non-cognitive traits, course engagement, and AI interaction patterns were collected and analyzed. Using hierarchical cluster analysis, the students were classified into three distinct groups: active questioners, responsive navigators, and silent listeners. Epistemic network analysis was then applied to further delineate the interaction profiles and cognitive engagement of different types of learners. The findings underscore how different learner types engage with human-AI interactive learning and offer practical implications for the design of adaptive educational systems.