用眼动数据分析提升语文教学，让教师轻松读懂学生注意力。
Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
- 基于用户中心设计，用可视化与故事化方式呈现眼动数据。
- 结合自然语言交互的智能助手，降低教师解读数据的认知负担。
- 适合教育科技开发者和一线语文教师参考使用。
眼动追踪能提供学生认知与参与度的深层洞察，但因数据解释困难且难以获取，尚未广泛应用于面向课堂的教育技术中。本文通过五项研究，迭代设计并评估了一款面向英语语言艺术（ELA）教学的眼动学习分析仪表盘。基于用户中心设计与数据叙事原则，探索了眼动数据如何支持教学反思、形成性评估与教学决策。研究发现，当眼动数据通过熟悉可视化、分层解释与叙事支架呈现时，可具备可及性与教学价值。此外，我们还展示了由大语言模型（LLM）驱动的对话式智能代理，可通过自然语言交互降低教师理解多模态学习分析数据的认知门槛。最后，论文提出未来教育科技系统在课堂环境中整合新型数据模态的设计启示。
原文摘要 · Abstract (English)
Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data can support reflection, formative assessment, and instructional decision-making. Our findings demonstrate that gaze analytics can be approachable and pedagogically valuable when supported by familiar visualizations, layered explanations, and narrative scaffolds. We further show how a conversational agent, powered by a large language model (LLM), can lower cognitive barriers to interpreting gaze data by enabling natural language interactions with multimodal learning analytics. We conclude with design implications for future EdTech systems that aim to integrate novel data modalities in classroom contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。