面向英语语言艺术（ELA）教学的眼动分析设计：一款具备对话式AI支持的以用户为中心的仪表盘
Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
AI总结：
本研究针对眼动数据在课堂教育技术中应用不足的问题，通过五项师生参与的研究迭代设计了带LLM驱动对话智能体的ELA眼动学习分析仪表盘，验证了其教学价值并给出设计启示。
摘要：
眼动追踪能为学生认知与参与度研究提供丰富洞见，但由于数据解读和可及性方面的挑战，其在面向课堂的教育技术中仍未得到充分利用。本文提出了一款面向英语语言艺术（ELA）的基于眼动的学习分析仪表盘的迭代设计与评估成果，该仪表盘通过五项涉及教师和学生的研究开发而成。我们以用户中心设计和数据叙事原则为指导，探索了眼动数据如何支持教学反思、形成性评估和教学决策。研究结果表明，在熟悉的可视化效果、分层解释和叙事支架的支持下，眼动分析既易于理解，又具有教学价值。我们进一步展示了由大语言模型（LLM）驱动的对话智能体如何通过支持与多模态学习分析的自然语言交互，降低解读眼动数据的认知障碍。最后，我们总结了未来旨在将新型数据模态融入课堂场景的教育科技（EdTech）系统的设计启示。
英文摘要：
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.
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。
