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Design  2024 

基于魅力工学的语言学习机造型优化设计研究
Research on Optimization Design of Language Learning Machine Based on Charm Engineering

DOI: 10.12677/design.2024.92262, PP. 694-702

Keywords: 评价构造法,魅力工学,语言学习机,功能可视化,优化设计研究
Evaluative Construction Method
, Charm Engineering, Language Learning Machine, Functional Visualization, Optimization Design Research

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Abstract:

目的:通过研究用户偏好的语言学习机造型设计元素,设计出符合用户需求的语言学习机造型,从而给予用户更高品质的学习体验。方法:首先对市面上已有的语言学习机造型图片进行收集筛选,将其外观拆分成实验样图,然后拿筛选好的样图对被试者进行访谈,同时进行线上问卷调查,根据访谈及问卷的结果绘制评价构造图。通过评价构造法发掘出学习机各部分的具体魅力特征,分析筛选整理出最具代表性的用户偏好风格,从而精准地提取用户偏好的语言学习机的魅力因素。最后在设计中融入功能可视化设计理念,并用调查得到的用户偏好作为参考依据进行语言学习机的造型设计。结论:通过对语言学习机外形设计要素与用户喜好关系的研究,得出用户喜好的特定外形设计要素,创新设计了语言学习机的外形造型,并验证了这种方法的有效性。
Purpose: Through the study of the design elements of language learning machine styling preferred by users, to design a language learning machine styling that meets the needs of users, so as to give users a higher quality learning experience. Methods: Firstly, we collect and screen the existing language learning machine modeling pictures on the market, split their appearance into experimental sample drawings, then interview the subjects with the screened sample drawings and conduct an online questionnaire survey at the same time, and then draw evaluation construction diagrams based on the results of the interviews and questionnaires. Through the evaluation construction method, the specific charm characteristics of each part of the learning machine are discovered, and the most representative user preference styles are analyzed and screened, so as to accurately extract the charm factors of the language learning machine preferred by users. Finally, we integrate the concept of functional visualization design into the design and use the user preferences obtained from the survey as a reference basis for the shape design of the language learning machine. Conclusion: Through the research on the relationship between the shape design elements of language learning machines and user preferences, the specific shape design elements of user preferences are derived, the shape modeling of language learning machines is innovatively designed, and the effectiveness of this method is verified.

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