LLMs作为教育分析师：将多模态数据轨迹转化为可执行的阅读评估报告
LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports
AI总结：
本研究利用多模态数据和LLM生成可执行的阅读评估报告，发现LLM能有效转化为教师友好的洞察，但需人类监督以确保可靠性与公平性。
摘要：
阅读评估对于提升学生的理解能力至关重要，然而许多教育科技应用主要关注基于结果的指标，对学生行为和认知的洞察有限。本研究探讨了多模态数据源的使用——包括眼动追踪数据、学习成果、评估内容和教学标准——以得出有意义的阅读洞察。我们采用无监督学习技术来识别不同的阅读行为模式，然后由大型语言模型（LLM）将得出的信息合成为可执行的报告供教育工作者使用，从而简化解释过程。LLM专家和人类教育工作者对这些报告的清晰度、准确性、相关性和教学实用性进行了评估。我们的研究结果表明，LLMs可以有效地充当教育分析师，将多样化的数据转化为深受教育工作者欢迎的、对教师友好的洞察。尽管在自动化洞察生成方面前景广阔，但人类的监督对于确保可靠性和公平性仍然至关重要。这项研究推进了教育领域以人为本的AI，将数据驱动的分析与实际的课堂应用联系起来。
英文摘要：
Reading assessments are essential for enhancing students' comprehension, yet many EdTech applications focus mainly on outcome-based metrics, providing limited insights into student behavior and cognition. This study investigates the use of multimodal data sources -- including eye-tracking data, learning outcomes, assessment content, and teaching standards -- to derive meaningful reading insights. We employ unsupervised learning techniques to identify distinct reading behavior patterns, and then a large language model (LLM) synthesizes the derived information into actionable reports for educators, streamlining the interpretation process. LLM experts and human educators evaluate these reports for clarity, accuracy, relevance, and pedagogical usefulness. Our findings indicate that LLMs can effectively function as educational analysts, turning diverse data into teacher-friendly insights that are well-received by educators. While promising for automating insight generation, human oversight remains crucial to ensure reliability and fairness. This research advances human-centered AI in education, connecting data-driven analytics with practical classroom applications.
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。
