Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems
Micro-entries: Encouraging Deeper Evaluation of Mental Models Over Time for Interactive Data Systems
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DOI：
10.1109/beliv51497.2020.00012
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Jeremy E. Block;E. Ragan
中科院分区：
文献类型：
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作者：
Jeremy E. Block;E. Ragan
许多交互式数据系统将数据的可视化表示与用于自动化和数据探索的嵌入式算法支持相结合。为了有效地支持透明和可解释的数据系统，研究人员和设计人员必须了解用户如何理解系统。讨论了系统逻辑用户心智模型的评价问题。心智模型很难捕捉和分析。虽然常见的评估方法的目的是近似用户的最终心理模型后，一段时间的系统使用，用户的理解不断演变，用户与系统随着时间的推移进行交互。在本文中，我们回顾了许多常见的心理模型测量技术，讨论了权衡，并建议方法更深入，更有意义的评估心理模型时，使用交互式数据分析和可视化系统。我们提出了随着时间的推移评估心理模型的指导方针，以帮助跟踪特定模型更新的演变，以及它们如何映射到界面功能和数据查询的特定使用。通过要求用户描述他们知道什么以及他们是如何知道的，研究人员可以收集结构化的，时间顺序的洞察用户的概念化过程，同时也有助于引导用户自己的发现。
Many interactive data systems combine visual representations of data with embedded algorithmic support for automation and data exploration. To effectively support transparent and explainable data systems, it is important for researchers and designers to know how users understand the system. We discuss the evaluation of users’ mental models of system logic. Mental models are challenging to capture and analyze. While common evaluation methods aim to approximate the user’s final mental model after a period of system usage, user understanding continuously evolves as users interact with a system over time. In this paper, we review many common mental model measurement techniques, discuss tradeoffs, and recommend methods for deeper, more meaningful evaluation of mental models when using interactive data analysis and visualization systems. We present guidelines for evaluating mental models over time to help track the evolution of specific model updates and how they may map to the particular use of interface features and data queries. By asking users to describe what they know and how they know it, researchers can collect structured, time-ordered insight into a user’s conceptualization process while also helping guide users to their own discoveries.