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    科学领域:

    • 数据可视化 数据可视化
    • 人与计算机的交互
    • 教育测量教育的测量

    背景情况:

    • 可视化素养对于解释和创建数据表示至关重要.
    • 经验研究越来越依赖于可视化素养评估.
    • 现有的方法很难准确地定义和测量可视化素养.

    研究的目的:

    • 思考定义和测量可视化素养的复杂性.
    • 确定导致可视化素养评估成为"邪恶"问题的因素.
    • 为改善可视化素养测量提出可行的策略.

    主要方法:

    • 自我民族学反映. 自我民族学反映.
    • 14个内部采访与可视化素养测试用户和设计人员.
    • 对有效性的概念,操作和方法威胁的分析.

    主要成果:

    • 视觉化素养评估是一个"邪恶"的问题,因为它具有流动性.
    • 视觉化素养的构造与它的测量之间存在错位.
    • 确定了对测量有效性的几个威胁.

    结论:

    • 扩大可视化素养评估的测试范围和方法.
    • 提高可视化素养测试的生态有效性和可用性.
    • 促进跨学科的合作和持续的对话,以拥抱可视化素养的流动性.