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Computer Science > Human-Computer Interaction

arXiv:2509.03741 (cs)
[Submitted on 3 Sep 2025 ]

Title: Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support

Title: 为ELA教学设计注视分析:具有对话式AI支持的以用户为中心的仪表板

Authors:Eduardo Davalos, Yike Zhang, Shruti Jain, Namrata Srivastava, Trieu Truong, Nafees-ul Haque, Tristan Van, Jorge Salas, Sara McFadden, Sun-Joo Cho, Gautam Biswas, Amanda Goodwin
Abstract: 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.
Abstract: 眼动追踪能够提供关于学生认知和参与度的丰富见解,但由于数据解释和可访问性的挑战,在面向课堂的教育技术中仍未得到充分利用。 在本文中,我们介绍了基于眼动的数据学习分析仪表板在英语语言艺术(ELA)中的迭代设计和评估,该仪表板通过五个涉及教师和学生的研究所开发。 在以用户为中心的设计和数据叙事原则的指导下,我们探讨了眼动数据如何支持反思、形成性评估和教学决策。 我们的研究结果表明,当通过熟悉的可视化、分层解释和叙事支架来支持时,眼动分析可以易于使用且具有教学价值。 我们进一步展示了如何通过由大型语言模型(LLM)驱动的对话代理,降低解读眼动数据的认知障碍,从而实现与多模态学习分析的自然语言交互。 我们最后总结了未来旨在在课堂环境中整合新型数据模式的教育技术系统的設計建議。
Comments: 22 pages, 9 figures, 3 tables, submitted to IUI2026
Subjects: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI)
Cite as: arXiv:2509.03741 [cs.HC]
  (or arXiv:2509.03741v1 [cs.HC] for this version)
  https://doi.org/10.48550/arXiv.2509.03741
arXiv-issued DOI via DataCite

Submission history

From: Eduardo Davalos [view email]
[v1] Wed, 3 Sep 2025 22:01:14 UTC (6,290 KB)
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