用大模型将阅读行为数据转化为教师可用的分析报告
LLMs as Educational Analysts: Transforming Multimodal Data Traces into Actionable Reading Assessment Reports
- 用无监督学习识别学生阅读行为模式
- 大模型生成可指导教学的评估报告，获教师认可
- 适合教育科技开发者与一线教师参考
阅读评估对提升学生理解力至关重要，但许多教育科技应用仅关注结果指标，难以揭示学生的行为与认知过程。本研究利用多模态数据（包括眼动追踪、学习成果、测评内容与教学标准），通过无监督学习识别出不同的阅读行为模式，并由大语言模型（LLM）将这些信息整合为可操作的教育报告，供教师使用。多位LLM专家与人类教师对报告的清晰性、准确性、相关性及教学实用性进行评估。结果表明，大模型能有效充当教育分析师，将多元数据转化为教师友好的洞察，获得广泛认可。尽管自动化洞察生成前景广阔，但人类监督仍不可或缺，以确保结果的可靠与公平。该研究推动了教育中以人为中心的AI发展，实现数据驱动分析与实际教学场景的融合。
原文摘要 · Abstract (English)
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.
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