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    Mapping Novice Designer Behavior to Design Fixation in the Early-Stage Design Process

    Source: Journal of Mechanical Design:;2024:;volume( 146 ):;issue: 009::page 91401-1
    Author:
    Jia, Miao
    ,
    Jiang, Shuo
    ,
    Qi, Jin
    ,
    Hu, Jie
    DOI: 10.1115/1.4064649
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: In the engineering design process, design fixation significantly constrains the diversity of design solutions. Numerous studies have aimed to mitigate design fixation, yet determining its occurrence in real-time remains a challenge. This research seeks to systematically identify the emergence of fixation through the behavior of novice designers in the early stages of the design process. We conducted a laboratory study, involving 50 novice designers possessing engineering drafting skills. Their design processes were monitored via video cameras, with both their design solutions and physical behaviors recorded. Subsequently, expert evaluators categorized design solutions into three types: Fixation, Low-quality, and Innovative. We manually recorded the names and durations of 31 different physical behaviors observed in the videos, which were then coded and filtered. Meanwhile, we propose a filtering and calculation method for the behavior in the design process. From this, four fixation behaviors were identified using variance analysis (ANOVA): Touch Mouth (TM), Touch Head (TH), Rest Head in Hands (RH), and Hold Face in Hands (HF). Our findings suggest that continuous interaction between the hand and head, mouth, or face can be indicative of a fixation state. Finally, we developed a Behavior-Fixation model based on the Support Vector Machine (SVM) for stage fixation judgment tasks, achieving an accuracy rate of 85.6%. This machine-learning model outperforms manual assessment in speed and accuracy. Overall, our study offers promising prospects for assisting designers in recognizing and avoiding design fixation. These findings, coupled with our proposed computational techniques, provide valuable insights for the development of automated and intelligent design innovation systems.
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      Mapping Novice Designer Behavior to Design Fixation in the Early-Stage Design Process

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    contributor authorJia, Miao
    contributor authorJiang, Shuo
    contributor authorQi, Jin
    contributor authorHu, Jie
    date accessioned2024-04-24T22:42:07Z
    date available2024-04-24T22:42:07Z
    date copyright3/5/2024 12:00:00 AM
    date issued2024
    identifier issn1050-0472
    identifier othermd_146_9_091401.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295710
    description abstractIn the engineering design process, design fixation significantly constrains the diversity of design solutions. Numerous studies have aimed to mitigate design fixation, yet determining its occurrence in real-time remains a challenge. This research seeks to systematically identify the emergence of fixation through the behavior of novice designers in the early stages of the design process. We conducted a laboratory study, involving 50 novice designers possessing engineering drafting skills. Their design processes were monitored via video cameras, with both their design solutions and physical behaviors recorded. Subsequently, expert evaluators categorized design solutions into three types: Fixation, Low-quality, and Innovative. We manually recorded the names and durations of 31 different physical behaviors observed in the videos, which were then coded and filtered. Meanwhile, we propose a filtering and calculation method for the behavior in the design process. From this, four fixation behaviors were identified using variance analysis (ANOVA): Touch Mouth (TM), Touch Head (TH), Rest Head in Hands (RH), and Hold Face in Hands (HF). Our findings suggest that continuous interaction between the hand and head, mouth, or face can be indicative of a fixation state. Finally, we developed a Behavior-Fixation model based on the Support Vector Machine (SVM) for stage fixation judgment tasks, achieving an accuracy rate of 85.6%. This machine-learning model outperforms manual assessment in speed and accuracy. Overall, our study offers promising prospects for assisting designers in recognizing and avoiding design fixation. These findings, coupled with our proposed computational techniques, provide valuable insights for the development of automated and intelligent design innovation systems.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleMapping Novice Designer Behavior to Design Fixation in the Early-Stage Design Process
    typeJournal Paper
    journal volume146
    journal issue9
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4064649
    journal fristpage91401-1
    journal lastpage91401-14
    page14
    treeJournal of Mechanical Design:;2024:;volume( 146 ):;issue: 009
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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