设计简要结构与刺激保真度之间的联系研究以优化新颖性和实用性

IF 1.7 3区 工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
G. Koronis, Arlindo Silva, Jacob Kang Kai Siang, C. Yogiaman
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引用次数: 0

摘要

这个基于学术的调查侧重于识别有助于生成高效设计摘要的元素以及它们与草图练习的设计结果的相关性。比较了四种情况:基线组、抽象组、上下文信息组和给出各种示例解决方案的组。通过更深入的调查,我们试图找出学生的设计创造力与不同设计条件下的刺激排列之间的相关性。结果表明,情境信息组具有较高的刺激保真度,其新颖性得分较高,而抽象信息组的有用性得分较高。这些发现有助于设计简报的制定,其目标是激发设计成果的创造力,并检查它们与学生对设计练习的看法之间的关系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A study on the link between design brief structure and stimulus fidelity to optimize novelty and usefulness
Abstract This academic-based investigation is focused on identifying elements that contribute toward the generation of efficient design briefs and their correlation with design outcomes of a sketching exercise. Four conditions are compared: a baseline group, an abstract group, a contextual information group, and a group that was given various example solutions. Via more in-depth surveys, we sought to elicit correlations between the students’ design creativity and stimuli permutations of the different design conditions. Results show that the contextual information groups, which were presented with higher levels of stimulus fidelity, had higher novelty scores, while abstract groups performed well in usefulness. These findings contribute to the formulation of design briefs where the goal is to stimulate the creativity of design outcomes and examine their relationships with student's perceptions of design exercises.
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来源期刊
CiteScore
4.40
自引率
14.30%
发文量
27
审稿时长
>12 weeks
期刊介绍: The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.
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