基于眼动追踪的个性化抑制训练：提升教育游戏中的学生学习与教师评估
Personalized Inhibition Training with Eye-Tracking: Enhancing Student Learning and Teacher Assessment in Educational Games
AI总结：
本研究提出基于眼动追踪的ReStroop游戏分析框架，通过双阈值检测与风险评估识别儿童注意力缺陷，实现个性化干预，支持教育决策。
摘要：
眼动追踪（ET）有助于理解交互环境中的视觉注意和认知过程。本研究提出了一个针对抑制控制游戏（名为ReStroop游戏）的综合眼动追踪分析框架，该游戏是一项旨在通过回收主题分类任务提高儿童抑制控制技能的教育干预措施，用于教育评估，通过统一算法处理原始注视数据，进行注视检测、表现评估和个性化干预规划。该系统采用双阈值眼动检测（I-VT和高级聚类）、全面的感兴趣区域（AOI）分析以及基于证据的风险评估，将注视模式转化为可操作的教育见解。我们在三个难度级别上评估了该框架，并揭示了关键的注意力缺陷，包括任务相关性低、注意力分散度高和处理效率受损。多维风险评估识别出高风险至中等风险水平，触发个性化干预，包括专注力训练、注意力调节支持和环境调整。该系统成功区分了适应性学习和认知过载，为教育干预提供了早期预警指标。结果表明，该系统在客观注意力评估、早期风险识别以及为学生、教师和专家生成基于证据的建议方面具有有效性，支持数据驱动的教育决策和个性化学习方法。
英文摘要：
Eye tracking (ET) can help to understand visual attention and cognitive processes in interactive environments. This study presents a comprehensive eye-tracking analysis framework of the Inhibitory Control Game, named the ReStroop game, which is an educational intervention aimed at improving inhibitory control skills in children through a recycling-themed sorting task, for educational assessment that processes raw gaze data through unified algorithms for fixation detection, performance evaluation, and personalized intervention planning. The system employs dual-threshold eye movement detection (I-VT and advanced clustering), comprehensive Area of Interest (AOI) analysis, and evidence-based risk assessment to transform gaze patterns into actionable educational insights. We evaluated this framework across three difficulty levels and revealed critical attention deficits, including low task relevance, elevated attention scatter, and compromised processing efficiency. The multi-dimensional risk assessment identified high to moderate risk levels, triggering personalized interventions including focus training, attention regulation support, and environmental modifications. The system successfully distinguishes between adaptive learning and cognitive overload, providing early warning indicators for educational intervention. Results demonstrate the system's effectiveness in objective attention assessment, early risk identification, and the generation of evidence-based recommendations for students, teachers, and specialists, supporting data-driven educational decision-making and personalized learning approaches.
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。
