一种名为“语境互联”的设计工具的用户体验与使用效果研究数据

This study recruited 25 designers with more than two years of product design learning experience and hold a 14-days-workshop. This study randomly separated them into Groups A and B, and instructed each group to begin a conceptual design project using the identical design proposal (design an entity product, which has the characteristics of a certain traditional Chinese cultural symbol and uses machine learning technology as one of the product functions). 12 designers made up Group A. They were instructed to use CC for their projects and were asked to quickly prototype by 3D printing for a short user walk-through. With a total of 13 members, Group B were asked to conduct product creative design process based on brainstorming without user study. Before the experiment began, the researcher introduced CC to Group A designers. A midterm display was scheduled for both groups A and B on the eighth day of the workshop to check the progress and answer the questions, and a presenting report was scheduled for the workshop's final day. All designers ultimately finished their work. Following the completion of the product creative designs by 25 designers, a questionnaire was developed to ask Group A to rate their experience on a seven-point Likert scale by answering three questions about "ease of use," two questions about "process of use," two questions about "creative outcomes," and five questions about "knowledge comprehension". On a seven-point Likert scale, all 25 designers were asked to rate how well they understood UCD and how creative they thought they were. Twelve designers from group A were also interviewed about the same four topics as the questionnaire, and their feedback and suggestions were solicited. Twenty industrial design master's or doctoral students were invited to assess the 25 concepts that were presented; all of them were not workshop participants. The 25 concepts were rated on three separate dimensions—"innovation of product function," "innovation of cultural symbol integration," and "overall evaluation of the concept"—using a seven-point Likert scale. This study also conducts a thorough investigation of three presented typical cases. This study conducted descriptive statistical analysis with the data from questionnaires by the software of jamovi (Version 1.6). 12 designers who used CC had high mean scores for the four aspects of ease of use, process of use, creative outcomes and knowledge comprehension (Ease of use M=5.47, SD=1.01. Process of use M=5.46, SD=0.940. Creative outcomes M=5.42, SD=0.900. Knowledge comprehension M=5.75, SD=0.832) . In addition, among all 25 designers, the mean scores of designers who used CC on " understanding of UCD" and " Self-evaluation of creativity " (UCD understanding M=5.75, SD=1.06. Self-evaluation of creativity M=5.25, SD=1.14) were higher than those who did not use CC (UCD understanding M=5.54, SD=0.967. Self-evaluation of creativity M=5.08, SD=1.50). Independent sample T-test and descriptive statistics were performed on the data of design outcomes using the software of jamovi (Version 1.6). The hypothesis is that, the degree of innovation in machine learning functions, cultural symbol integration, and overall concept innovation from other designers are significantly higher in the outcomes of designers who used CC than in the outcomes of designers who did not use CC. The Shapiro-Wilk normality test was passed on all three variables (W is close to 1, p>0.05). The group that used CC (ML function creation Mdn=4.60. Overall evaluation Mdn=4.45) scored significantly higher on evaluations of machine learning function innovation (ML function creation) and overall concept innovation (Overall evaluation) than the group that did not use CC (ML function creation Mdn=4.25. Overall evaluation Mdn=3.95) (ML function creation p=0.004. Overall evaluation p=0.017) . The difference in mean values for the evaluation of innovation in cultural symbol integration between the two groups was not statistically significant (Cultural symbols integration p=0.467), but descriptive statistics indicated that the mean value for the group using CC (M=4.19, SD=0.455) was slightly higher than the mean value for the group.

本研究招募了25名拥有两年以上产品设计学习经验的设计师，并开展了为期14天的工作坊。将受试者随机分为A、B两组，要求两组基于同一设计提案开展概念设计项目：设计一款兼具中国传统文化符号特征、并将机器学习技术作为产品功能之一的实体产品。A组共12名设计师，要求其使用CC开展设计，并通过3D打印快速制作原型以开展简短的用户试用。B组共13名设计师，要求其基于头脑风暴开展产品创意设计流程，且不进行用户研究。实验开始前，研究者向A组设计师介绍了CC。工作坊第8天为两组安排了中期展示环节，以核查进度并解答疑问，工作坊最后一天则安排了成果汇报。所有设计师均顺利完成了设计任务。25名设计师完成产品创意设计后，研究人员编制问卷，要求A组设计师基于7点李克特量表（Likert scale）完成评分，问卷包含3道关于"易用性"、2道关于"使用流程"、2道关于"创意成果"以及5道关于"知识理解"的问题。同时要求全部25名设计师基于7点李克特量表，分别对自身对用户中心设计（User-Centered Design, UCD）的理解程度以及自我创意水平进行评分。此外，研究人员还对A组的12名设计师进行了访谈，访谈涵盖与问卷相同的4个主题，并征集了他们的反馈与建议。邀请20名工业设计硕士或博士研究生对提交的25份设计概念进行评估，所有评估者均未参与本次工作坊。评估采用7点李克特量表，从"产品功能创新性""文化符号融合创新性"以及"概念整体评价"三个维度进行评分。本研究还对3个典型展示案例进行了深入分析。本研究使用jamovi软件（版本1.6）对问卷数据进行描述性统计分析。使用CC的12名设计师在易用性、使用流程、创意成果与知识理解四个维度上均获得了较高的平均得分（易用性M=5.47，SD=1.01；使用流程M=5.46，SD=0.940；创意成果M=5.42，SD=0.900；知识理解M=5.75，SD=0.832）。此外，在全部25名设计师中，使用CC的设计师在"UCD理解程度"与"创意自我评估"两项上的平均得分（UCD理解程度M=5.75，SD=1.06；创意自我评估M=5.25，SD=1.14）均高于未使用CC的设计师（UCD理解程度M=5.54，SD=0.967；创意自我评估M=5.08，SD=1.50）。本研究使用jamovi软件（版本1.6）对设计成果数据进行独立样本t检验与描述性统计分析，研究假设为：使用CC的设计师所产出的设计成果，在机器学习功能创新性、文化符号融合创新性以及整体概念创新性三个维度上，均显著高于未使用CC的设计师。三项变量均通过了Shapiro-Wilk正态性检验（W值接近1，p>0.05）。使用CC的组别在机器学习功能创新性（ML功能创新Mdn=4.60）与整体概念创新性（整体评价Mdn=4.45）的评分上，均显著高于未使用CC的组别（ML功能创新Mdn=4.25；整体评价Mdn=3.95）（ML功能创新p=0.004；整体评价p=0.017）。两组在文化符号融合创新性评分上的均值差异无统计学意义（文化符号融合p=0.467），但描述性统计结果显示，使用CC的组别平均得分（M=4.19，SD=0.455）略高于未使用CC的组别。