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    科学领域:

    • 数据可视化 数据可视化
    • 学习科学 学习科学
    • 人与计算机的交互

    背景情况:

    • 目前用于数据可视化理解的评估方法主要依赖于性能测试,例如价值检索任务.
    • 这些方法往往忽略了影响理解的关键因素,包括计算能力,熟悉图形类型和审美感知.
    • 现有的仪器不足以捕捉可视化理解的多面性质.

    研究的目的:

    • 设计和验证一个全面的评估工具,可视化理解多维评估方法 (MdamV).
    • 为了整合基于任务的绩效指标与自我感知能力和定性批评进行整体评估.
    • 探索视觉化理解作为一个基于学习科学的多方面的过程.

    主要方法:

    • 开发了可视化理解的多维评估方法 (MdamV),包括六个维度:理解,解码,美化,批评,阅读和上下文化.
    • 在奥地利对代表性调查样本 (N=438) 进行了MdamV的管理,使用了以线条和条形图表形式呈现的气候数据.
    • 收集了有关任务执行,自我评估的算力,图表熟悉度和开放式批评的数据.

    主要成果:

    • 验证数据显示,大约25%的受访者难以理解基本数据单元.
    • 大约20%的参与者表示不熟悉所展示的图表类型 (线条和条形图).
    • 自我评估的计算能力与数据阅读性能有显著的正相关性 (p=0.0004).

    结论:

    • 数据可视化理解的多维评估方法 (MdamV) 提供了一个更完整的评估,个人如何理解数据可视化.
    • 视觉化理解是一个局部过程,受个人因素和正在分析的特定视觉化的影响,超出了单纯的任务执行范围.
    • 未来的评估应纳入多维方法,以捕捉可视化理解的全部范围.