用眼动追踪识别真实课堂中的阅读行为，提升教学评估精准度。
Beyond Instructed Tasks: Recognizing In-the-Wild Reading Behaviors in the Classroom Using Eye Tracking
- 结合理论模型与AI分类器，从眼动速度、密度和序列性区分阅读行为。
- 轻量级2D CNN在真实场景下实现0.8的F1分数，准确识别三种阅读模式。
- 适用于教育研究者与智能教学系统，助力个性化学习分析。
理解浏览、深度阅读和扫描等阅读行为对改进教学至关重要。以往眼动研究多依赖受控指令任务，易改变自然行为，难以推广至真实场景。此外，阅读行为类型缺乏清晰定义。本研究通过教室实验收集指令式与真实场景下的阅读数据，提出混合方法框架：包含人工驱动的理论模型、统计分析与AI分类器，基于眼动速度、密度与序列性区分阅读行为。所提出的轻量级2D CNN模型在行为识别上取得0.8的F1分数，为理解真实课堂中的阅读行为提供了可靠方法。该研究增强了教育者获取精细行为洞察的能力，支持更精准的教学评估与干预。
原文摘要 · Abstract (English)
Understanding reader behaviors such as skimming, deep reading, and scanning is essential for improving educational instruction. While prior eye-tracking studies have trained models to recognize reading behaviors, they often rely on instructed reading tasks, which can alter natural behaviors and limit the applicability of these findings to in-the-wild settings. Additionally, there is a lack of clear definitions for reading behavior archetypes in the literature. We conducted a classroom study to address these issues by collecting instructed and in-the-wild reading data. We developed a mixed-method framework, including a human-driven theoretical model, statistical analyses, and an AI classifier, to differentiate reading behaviors based on their velocity, density, and sequentiality. Our lightweight 2D CNN achieved an F1 score of 0.8 for behavior recognition, providing a robust approach for understanding in-the-wild reading. This work advances our ability to provide detailed behavioral insights to educators, supporting more targeted and effective assessment and instruction.
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