AI 中文总结
针对现有视频学习注意力分析算法易受噪声干扰、教师修订成本高的问题，提出基于多模态脑信号的注意力建模框架，开发整合视听注意力的SAVVY可视化系统，经验证可有效辅助教学视频优化。
AI 中文摘要
视频学习（Video-Based Learning, VBL）在过去十年成为流行的教育传播媒介，涵盖在线教育到混合学习。学生对视频质量的期望不断提高，促使教师在发布教学视频前优化设计。提前分析试点学生的注意力已成为指导课程改进的常规优化策略。然而，现有注意力量化算法极易受现实环境中的噪声影响，降低估计准确性；即便有注意力数据，教师仍需投入大量精力进行经验性修订尝试，限制了实际可行性。为应对这些挑战，我们首先提出一种基于多模态脑信号的新型注意力建模框架，可稳定追踪学生注意力水平；随后开发SAVVY，一种整合视觉与听觉注意力的新型交互式视觉分析系统，支持自上而下探索学生注意力变化。SAVVY包含三个协调的可视化模块，融合课程内容结构、视听信息密度、注意力资源分配等多层面信息，提供单个学生多时间分辨率的注意力轨迹，使教师能全面分析注意力波动的根本原因，为后续教学视频改进提供依据。我们通过定量实验、两个案例研究和专家访谈对SAVVY进行评估，结果表明SAVVY在直观识别学生注意力变化、支持教学视频优化方面具有有效性和可用性。
英文摘要
Video-Based Learning (VBL) has become a popular delivery medium of education in the past decade, ranging from online education to hybrid learning. Students' rising expectations for video quality have motivated teachers to enhance the design of instructional videos before releasing them. Analyzing the attention of pilot cohorts in advance has become a conventional optimization strategy to guide course improvement. However, existing attention quantification algorithms are highly susceptible to noise in real-world environments, degrading estimation accuracy. Moreover, even when attention data are available, teachers must still invest substantial effort in empirical revision attempts, limiting practical feasibility. To address these challenges, we first propose a novel attention modeling framework based on multimodal brain signals that enables stable tracking of student attention levels. We then develop SAVVY, a novel interactive visual analytics system that integrates visual and auditory attention to support top-down exploration of student attention variations. SAVVY comprises three coordinated visualization modules. These modules incorporate multi-level information, including course content structure, audiovisual information density, and attentional resource allocation, and provide multi-temporal-resolution attention trajectories of individual students, enabling teachers to comprehensively analyze the underlying causes of attention fluctuations and inform their subsequent instructional video improvement. We evaluate SAVVY through quantitative experiments, two case studies, and expert interviews. The results demonstrate the effectiveness and usability of SAVVY in intuitively identifying student attention variations and supporting instructional video optimization.
