Abstract
This study utilized cluster analysis and the online collaboration whiteboard platform Miro to help students identify and classify the exterior features of electric vehicles, thereby enhancing their online collaboration skills in design classification and complex data analysis, leading to increased efficiency and accuracy. The study was conducted in four main stages: first, students collected 120 representative front-view images of electric vehicles; next, they categorized six key car parts (headlights, front grille, lower grille, windshield, fog lights, and side mirrors) on Miro using an expert-driven method, with each part classified into five design feature styles. Then, hierarchical cluster analysis using SPSS, where the dendrogram generated from the data, identified five optimal clusters; finally, the clusters were divided into five groups using the K-Means clustering method. The ANOVA (Analysis of Variance) results showed significant differences in the selected features between different clusters, validating the effectiveness of the classification method and the clustering results. The results of this study demonstrated that, through expert classification and SPSS hierarchical cluster analysis, five clusters with significant differences were ultimately formed. Therefore, this study successfully conducted online collaborative classification through Miro and applied cluster analysis to perform detailed classification and analysis of the design features of 120 electric vehicle front-view images. This approach not only enhanced students' abilities in identifying and classifying design features but also cultivated their skills in online collaboration and data analysis. Additionally, it provided valuable insights for automotive designers in understanding and applying design feature differences to meet market demands.
Author Information
Yan-Bo Huang, National Cheng Kung University, Taiwan
Meng-Dar Shieh, National Cheng Kung University, Taiwan
Paper Information
Conference: IICE2025
Stream: Design
This paper is part of the IICE2025 Conference Proceedings (View)
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To cite this article:
Huang Y., & Shieh M. (2025) Cluster Analysis of Electric Vehicle Exterior Features: An Educational Study Using Miro for Online Collaboration and Data Analysis ISSN: 2189-1036 – The IAFOR International Conference on Education – Hawaii 2025 Official Conference Proceedings (pp. 253-263) https://doi.org/10.22492/issn.2189-1036.2025.22
To link to this article: https://doi.org/10.22492/issn.2189-1036.2025.22
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