Comparative evaluations of visualization onboarding methods

IF 3.8 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Visual Informatics Pub Date : 2022-12-01 DOI:10.1016/j.visinf.2022.07.001
Christina Stoiber , Conny Walchshofer , Margit Pohl , Benjamin Potzmann , Florian Grassinger , Holger Stitz , Marc Streit , Wolfgang Aigner
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Abstract

Comprehending and exploring large and complex data is becoming increasingly important for a diverse population of users in a wide range of application domains. Visualization has proven to be well-suited in supporting this endeavor by tapping into the power of human visual perception. However, non-experts in the field of visual data analysis often have problems with correctly reading and interpreting information from visualization idioms that are new to them. To support novices in learning how to use new digital technologies, the concept of onboarding has been successfully applied in other fields and first approaches also exist in the visualization domain. However, empirical evidence on the effectiveness of such approaches is scarce. Therefore, we conducted three studies with Amazon Mechanical Turk (MTurk) workers and students investigating visualization onboarding at different levels: (1) Firstly, we explored the effect of visualization onboarding, using an interactive step-by-step guide, on user performance for four increasingly complex visualization techniques with time-oriented data: a bar chart, a horizon graph, a change matrix, and a parallel coordinates plot. We performed a between-subject experiment with 596 participants in total. The results showed that there are no significant differences between the answer correctness of the questions with and without onboarding. Particularly, participants commented that for highly familiar visualization types no onboarding is needed. However, for the most unfamiliar visualization type — the parallel coordinates plot — performance improvement can be observed with onboarding. (2) Thus, we performed a second study with MTurk workers and the parallel coordinates plot to assess if there is a difference in user performances on different visualization onboarding types: step-by-step, scrollytelling tutorial, and video tutorial. The study revealed that the video tutorial was ranked as the most positive on average, based on a sentiment analysis, followed by the scrollytelling tutorial and the interactive step-by-step guide. (3) As videos are a traditional method to support users, we decided to use the scrollytelling approach as a less prevalent way and explore it in more detail. Therefore, for our third study, we gathered data towards users’ experience in using the in-situ scrollytelling for the VA tool Netflower. The results of the evaluation with students showed that they preferred scrollytelling over the tutorial integrated in the Netflower landing page. Moreover, for all three studies we explored the effect of task difficulty. In summary, the in-situ scrollytelling approach works well for integrating onboarding in a visualization tool. Additionally, a video tutorial can help to introduce interaction techniques of visualization.

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可视化入职方法的比较评价
理解和探索大型和复杂的数据对于广泛应用领域的不同用户群体变得越来越重要。可视化已被证明非常适合通过利用人类视觉感知的力量来支持这一努力。然而,可视化数据分析领域的非专家在正确阅读和解释可视化习语中的信息时经常遇到问题,这些习语对他们来说是新的。为了帮助新手学习如何使用新的数字技术,入职的概念已经成功地应用于其他领域,并且在可视化领域也存在第一种方法。然而,关于这些方法有效性的经验证据很少。因此,我们对亚马逊土耳其机械公司(MTurk)的员工和学生进行了三项研究,调查了不同层次的可视化入职情况:(1)首先,我们使用交互式分步指南,探索了可视化入职对四种日益复杂的可视化技术(柱状图、水平图、变化矩阵和平行坐标图)的用户绩效的影响。我们进行了一个共有596名参与者的受试者间实验。结果表明,有和没有入职的问题的答案正确性没有显著差异。与会者特别指出,对于非常熟悉的可视化类型,不需要入职培训。然而,对于最不熟悉的可视化类型——平行坐标图——性能改进可以通过入职观察到。(2)因此,我们对MTurk员工和平行坐标图进行了第二次研究,以评估在不同的可视化入职类型(分步教学、滚动教学和视频教学)上,用户的表现是否存在差异。研究显示,基于情感分析,视频教程被评为平均最积极的,其次是卷轴式教程和交互式循序渐进指南。(3)由于视频是一种传统的支持用户的方法,我们决定使用一种不太流行的方式,并对其进行更详细的探索。因此,在我们的第三项研究中,我们收集了用户使用VA工具Netflower的现场滚动显示体验的数据。对学生的评估结果显示,他们更喜欢卷轴式讲述,而不是集成在Netflower登陆页面的教程。此外,在所有三项研究中,我们都探讨了任务难度的影响。总而言之,在可视化工具中集成现场滚动显示方法非常有效。此外,视频教程可以帮助介绍可视化的交互技术。
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来源期刊
Visual Informatics
Visual Informatics Computer Science-Computer Graphics and Computer-Aided Design
CiteScore
6.70
自引率
3.30%
发文量
33
审稿时长
79 days
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