"Merging Results Is No Easy Task": An International Survey Study of Collaborative Data Analysis Practices Among UX Practitioners

要旨

Analysis is a key part of usability testing where UX practitioners seek to identify usability problems and generate redesign suggestions. Although previous research reported how analysis was conducted, the findings were typically focused on individual analysis or based on a small number of professionals in specific geographic regions. We conducted an online international survey of 279 UX practitioners on their practices and challenges while collaborating during data analysis. We found that UX practitioners were often under time pressure to conduct analysis and adopted three modes of collaboration: independently analyze different portions of the data and then collaborate, collaboratively analyze the session with little or no independent analysis, and independently analyze the same set of data and then collaborate. Moreover, most encountered challenges related to lack of resources, disagreements with colleagues regarding usability problems, and difficulty merging analysis from multiple practitioners. We discuss design implications to better support collaborative data analysis.

著者
Emily Kuang
Rochester Institute of Technology, Rochester, New York, United States
Xiaofu Jin
Hong Kong University of Science and Technology, Guangzhou, Guangdong, China
Mingming Fan
Hong Kong University of Science and Technology, Guangzhou, Guangdong, China
論文URL

https://dl.acm.org/doi/abs/10.1145/3491102.3517647

動画

会議: CHI 2022

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2022.acm.org/)

セッション: Interaction Schemes and Patterns II

293
5 件の発表
2022-05-03 23:15:00
2022-05-04 00:30:00