AI 中文总结
通过40人被试间观察研究，分析Jupyter、Excel等四类数据整理工具的界面范式对用户策略的影响，发现工具可供性引导策略但不决定结果，工具设计对数据工作有结构性作用。
AI 中文摘要
数据整理在任何数据项目中都占用了不成比例的工作量。尽管有多种工具支持数据整理，但人们对其不同的界面形式如何影响实际整理方式知之甚少。我们开展了一项被试间（N=40）观察研究，对象是使用不同界面范式工具执行数据清理任务的情况，这些工具包括Jupyter（笔记本）、Excel（电子表格）、ChatGPT（对话式AI）和OpenRefine（可视化整理工具）。我们将观察结果置于“编程系统技术维度”框架中，以此作为跨界面范式比较的概念支架。在本研究背景下，结果表明，工具的可供性引导用户策略，但不决定结果。没有任何单一工具具备一致优势，也未观察到工具在各结果指标上的结果趋同。相反，我们识别出权衡关系并将其与观察到的实践联系起来。例如，一个关键张力在于以数据为中心和以抽象为中心的界面之间：以数据为中心的界面鼓励机会主义式清理，而非以抽象为重点的工具中所发现的系统性、有计划的转换（但会带来认知负担）。工具设计超越了单纯的功能，在数据工作的开展方式中发挥着结构性作用。
英文摘要
Wrangling consumes a disproportionate share of the effort associated with any data project. While a variety of tools support it, relatively little is known about how their differing interface forms shape the way people actually wrangle. We conduct a between-subjects (N=40) observational study of data cleaning tasks performed in tools spanning distinct interface paradigms: Jupyter (notebook), Excel (spreadsheet), ChatGPT (conversational AI), and OpenRefine (visual wranglers). We situate our observations within the Technical Dimensions of Programming Systems framework, which we use as a conceptual scaffold for comparing across interface paradigms. Within the context of our study, the results suggest that tool affordances steer user strategies but do not determine outcomes. There is no consistent advantage of any single tool, nor convergence of results within tools observed across our outcome measures. Instead, we identify trade-offs and connect them with observed practice. For example, a key tension is between data- and abstraction-centered interfaces, where data-centered interfaces encourage opportunistic cleaning rather than systematic, planned transformations found in abstraction-focused tools (but come with a cognitive burden). Tool design, beyond mere functionality, plays a structuring role in how data work unfolds.
