论文标题
Visquiz:探索反馈机制以提高图形感知
VisQuiz: Exploring Feedback Mechanisms to Improve Graphical Perception
论文作者
论文摘要
图形感知研究是可视化研究的关键要素,构成了设计建议的基础,并有助于我们对人们如何理解可视化的理解。但是,图形感知研究通常仅包括简短的培训课程,而更长,更深入的反馈的影响仍然不清楚。在本文中,我们探讨了用于图形感知任务的反馈的设计和评估,称为Visquiz。使用测验样隐喻,我们为典型的可视化比较实验设计了反馈,在每个试验中,在动画序列中显示了参与者的答案以及正确的答案。我们将这个测验隐喻扩展到实验的每个阶段之后包括摘要反馈,为参与者提供了更多的时刻,以反思他们的表现。为了评估Visquiz,我们进行了一个受试者之间的实验,包括40个试验的三个阶段,每个试验和对照条件仅包括摘要反馈。 n = 80名参与者的结果表明,一旦参与者开始收到试验反馈(第2阶段),他们在气泡图表中的表现明显优于控制条件下的泡沫。消除反馈时会持续这种效果(第3阶段)。结果还表明,由于反馈而提高了性能的总体趋势。我们在其他可视化素养工作的背景下讨论了这些发现,以及在可视化,反馈和学习的交集中可能的未来工作。实验数据和分析脚本可在以下存储库中获得https://osf.io/jys5d/
Graphical perception studies are a key element of visualization research, forming the basis of design recommendations and contributing to our understanding of how people make sense of visualizations. However, graphical perception studies typically include only brief training sessions, and the impact of longer and more in-depth feedback remains unclear. In this paper, we explore the design and evaluation of feedback for graphical perception tasks, called VisQuiz. Using a quiz-like metaphor, we design feedback for a typical visualization comparison experiment, showing participants their answer alongside the correct answer in an animated sequence in each trial. We extend this quiz metaphor to include summary feedback after each stage of the experiment, providing additional moments for participants to reflect on their performance. To evaluate VisQuiz, we conduct a between-subjects experiment, including three stages of 40 trials each with a control condition that included only summary feedback. Results from n = 80 participants show that once participants started receiving trial feedback (Stage 2) they performed significantly better with bubble charts than those in the control condition. This effect carried over when feedback was removed (Stage 3). Results also suggest an overall trend of improved performance due to feedback. We discuss these findings in the context of other visualization literacy efforts, and possible future work at the intersection of visualization, feedback, and learning. Experiment data and analysis scripts are available at the following repository https://osf.io/jys5d/