A Data Driven Tool to Support Design Team Composition Measuring Skills Diversity
Year: 2023
Editor: Kevin Otto, Boris Eisenbart, Claudia Eckert, Benoit Eynard, Dieter Krause, Josef Oehmen, Nadège Troussier
Author: Chiarello, Filippo (1,5); Spada, Irene (3,5); Barandoni, Simone (4,5); Giordano, Vito (1,5); Fantoni, Gualtiero (2,5)
Series: ICED
Institution: 1: School of Engineering, Department of Energy, Systems, Land and Construction Engineering, University of Pisa, Italy;2: School of Engineering, Department of Civil and Industrial Engineering, University of Pisa, Italy;3: School of Engineering, Department of Engineering Informatics, University of Pisa, Italy;4: Department of Informatics, University of Pisa, Italy;5: B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Section: Design Methods
Page(s): 0091-0100
DOI number: https://doi.org/10.1017/pds.2023.10
ISSN: 2732-527X
Abstract
hese reasons we propose a tool that aims to map the design skills of students to optimise team composition. The tool is based on a questionnaire grounded in the design theory and aims at measuring the willingness of students at performing certain design tasks. The results of the questionnaires are analysed using Principal Component Analysis to normalise each students’ answers to the whole class, and to show the distribution of students in the space of engineering design skills.
We present the design process of the tool, and a first experimentation on two classes of master’s degree students in Management Engineering and Data Science, testing the tool on a total of 72 students. The results are promising and demonstrate the robusteness of the questionnaire and of the analytical method. Also, we propose next steps for our research activity, calling for other researchers to test our method in different contexts.
Keywords: Teamwork, Design education, Research methodologies and methods, Data Analysis, Design Team Composition