Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors
Design Embedding: Representation Learning of Design Thinking to Cluster Design Behaviors
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DOI：
10.1115/detc2021-72406
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Molla Hafizur Rahman;Charles Xie;Zhenghui Sha
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文献类型：
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作者：
Molla Hafizur Rahman;Charles Xie;Zhenghui Sha
设计思维对于设计过程的成功至关重要，因为它通过指导设计决策来帮助实现设计目标。因此，从根本上理解设计思维对于改进设计方法、工具和理论至关重要。然而，解释设计思维是具有挑战性的，因为它是一个隐藏和无形的认知过程。在本文中，我们代表设计思维作为人类设计师的思维过程和他们的设计行为之间的中间层。为此，本文首先确定了五个设计行为的基础上，目前的设计理论。这些行为包括设计行为偏好、一步顺序行为、情境行为、长期顺序行为和反思性思维行为。接下来，我们开发计算方法来表征每个设计行为。特别地，我们使用设计动作分布、一阶马尔可夫链、Doc 2 Vec、双向LSTM自动编码器和时间间隔分布来表征这五种设计行为。通过嵌入技术对设计行为的表征本质上是设计思维的潜在表征，我们称之为设计嵌入。在获得嵌入之后，对每个嵌入采用X-均值聚类算法以聚类设计者。该方法适用于从高中太阳能系统设计挑战收集的数据。聚类结果表明，设计者根据相应的行为遵循多个设计模式，验证了利用设计嵌入进行设计行为聚类的有效性。基于该方法的设计嵌入提取可以用于其他设计研究，如推断设计决策，预测设计性能，识别设计行为识别。
Design thinking is essential to the success of a design process as it helps achieve the design goal by guiding design decision-making. Therefore, fundamentally understanding design thinking is vital for improving design methods, tools and theories. However, interpreting design thinking is challenging because it is a cognitive process that is hidden and intangible. In this paper, we represent design thinking as an intermediate layer between human designers’ thought processes and their design behaviors. To do so, this paper first identifies five design behaviors based on the current design theories. These behaviors include design action preference, one-step sequential behavior, contextual behavior, long-term sequential behavior, and reflective thinking behavior. Next, we develop computational methods to characterize each of the design behaviors. Particularly, we use design action distribution, first-order Markov chain, Doc2Vec, bi-directional LSTM autoencoder, and time gap distribution to characterize the five design behaviors. The characterization of the design behaviors through embedding techniques is essentially a latent representation of the design thinking, and we refer to it as design embeddings. After obtaining the embedding, an X-mean clustering algorithm is adopted to each of the embeddings to cluster designers. The approach is applied to data collected from a high school solar system design challenge. The clustering results show that designers follow several design patterns according to the corresponding behavior, which corroborates the effectiveness of using design embedding for design behavior clustering. The extraction of design embedding based on the proposed approach can be useful in other design research, such as inferring design decisions, predicting design performance, and identifying design actions identification.