为什么要改变我的设计:用可探索的解释解释糟糕的可视化设计
IEEE transactions on visualization and computer graphics
|October 27, 2023
概括
了解数据可视化最佳实践可以改善图表解释. 参与者学会识别欺骗性图表并接受设计变化,尽管解释方法在说服力上没有显著差异.
科学领域:
- 数据可视化 数据可视化
- 人与计算机的交互
- 信息设计信息设计
背景情况:
- 有效的数据可视化对于准确的数据表示至关重要,但许多人缺乏最佳实践的知识.
- 误导性图表可以欺骗观众,需要教育方法来促进诚实的视觉沟通.
- 现有的资源经常使用修辞手段来解释数据可视化原则.
研究的目的:
- 识别和评估用于教学数据可视化最佳实践的常见解释方法.
- 评估这些方法在改善图表设计和说服力方面的有效性.
- 引入和测试可探索解释对用户理解和设计接受的影响.
主要方法:
- 分析现有文献和设计研讨会,以确定六种常见的解释方法.
- 两项众包研究 (每项N=125) 评估了这些方法的教学和说服力.
- 引入可探索的解释,允许交互式图表操作.
主要成果:
- 在接受解释后,参与者表现出更好的识别欺骗性图表的能力.
- 观察到接受拟议的可视化设计更改的意愿显著 (超过60%).
- 在说服力方面,在各种解释方法之间没有发现统计学上显著的差异.
结论:
- 教育干预可以提高用户在检测误导性数据可视化方面的技能.
- 学习者很容易接受设计的改变,以提高图表的诚实性和清晰度.
- 可能需要进一步的研究来优化解释方法,以便在数据可视化教育中产生最大的说服力.
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