Impact of interaction technique in interactive data visualisations: A study on lookup, comparison, and relation-seeking tasks

IF 5.3 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

International Journal of Human-Computer Studies Pub Date : 2024-08-31 DOI:10.1016/j.ijhcs.2024.103359

Niels van Berkel , Benjamin Tag , Rune Møberg Jacobsen , Daniel Russo , Helen C. Purchase , Daniel Buschek

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引用次数: 0

Abstract

This paper presents an analysis of different interaction techniques used in interactive data visualisations to support end-users in visual analytics tasks. Our selection of interaction techniques is based on prior work and consists of the interaction techniques Select, Explore, Reconfigure, Encode, Filter, Abstract/Elaborate, and Connect. Through a within-subject study, we assessed participants’ abilities to utilise these techniques when faced with three distinct types of data-driven tasks; lookup, comparison, and Relation-seeking. Our research investigates the impact of these interaction techniques on the correctness, confidence, perceived difficulty, and cognitive load of N = 80 self-identified data scientists and N = 80 non-experts. We find that interaction technique significantly impacts answer correctness and participant confidence. Participants performed best across those interaction techniques that allow for information that is deemed least relevant to be concealed, which is reflected in lower intrinsic and extraneous cognitive load. Interestingly, participants’ expertise affected their confidence but not their accuracy. Our results provide insights useful for a more targeted and informed design and usage of interactive data visualisations.

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交互式数据可视化中交互技术的影响：关于查找、比较和关系搜索任务的研究

本文分析了交互式数据可视化中使用的不同交互技术，以支持终端用户完成可视化分析任务。我们对交互技术的选择是基于先前的工作，包括选择、探索、重新配置、编码、过滤、抽象/协作和连接等交互技术。通过主体内研究，我们评估了参与者在面对三种不同类型的数据驱动任务（查找、比较和关系搜索）时使用这些技术的能力。我们的研究调查了这些交互技术对 N = 80 名自我认同的数据科学家和 N = 80 名非专家的正确性、自信心、感知难度和认知负荷的影响。我们发现，交互技术对答案的正确性和参与者的信心有很大影响。在那些允许隐藏被认为最不相关的信息的交互技术中，参与者的表现最好，这反映在较低的内在和外在认知负荷上。有趣的是，参与者的专业知识会影响他们的信心，但不会影响他们的准确性。我们的研究结果为更有针对性地设计和使用交互式数据可视化提供了有益的启示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Human-Computer Studies 工程技术-计算机：控制论

CiteScore

11.50

自引率

5.60%

发文量

108

审稿时长

3 months

期刊介绍： The International Journal of Human-Computer Studies publishes original research over the whole spectrum of work relevant to the theory and practice of innovative interactive systems. The journal is inherently interdisciplinary, covering research in computing, artificial intelligence, psychology, linguistics, communication, design, engineering, and social organization, which is relevant to the design, analysis, evaluation and application of innovative interactive systems. Papers at the boundaries of these disciplines are especially welcome, as it is our view that interdisciplinary approaches are needed for producing theoretical insights in this complex area and for effective deployment of innovative technologies in concrete user communities. Research areas relevant to the journal include, but are not limited to: • Innovative interaction techniques • Multimodal interaction • Speech interaction • Graphic interaction • Natural language interaction • Interaction in mobile and embedded systems • Interface design and evaluation methodologies • Design and evaluation of innovative interactive systems • User interface prototyping and management systems • Ubiquitous computing • Wearable computers • Pervasive computing • Affective computing • Empirical studies of user behaviour • Empirical studies of programming and software engineering • Computer supported cooperative work • Computer mediated communication • Virtual reality • Mixed and augmented Reality • Intelligent user interfaces • Presence ...