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A DATA DRIVEN TOOL TO SUPPORT DESIGN TEAM COMPOSITION MEASURING SKILLS DIVERSITY

Published online by Cambridge University Press:  19 June 2023

Filippo Chiarello ,
Irene Spada ,
Simone Barandoni ,
Vito Giordano  and
Gualtiero Fantoni
Filippo Chiarello*
Affiliation:
School of Engineering, Department of Energy, Systems, Land and Construction Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Irene Spada
Affiliation:
School of Engineering, Department of Engineering Informatics, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Simone Barandoni
Affiliation:
Department of Informatics, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Vito Giordano
Affiliation:
School of Engineering, Department of Energy, Systems, Land and Construction Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
Gualtiero Fantoni
Affiliation:
School of Engineering, Department of Civil and Industrial Engineering, University of Pisa, Italy; B4DS - Business Engineering for Data Science lab, University of Pisa, Italy
*
Chiarello, Filippo, Università di Pisa, Italy, [email protected]

Abstract

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Team composition in Project Based Learning is the first task for the class and has a great impact on the learning experience. Anyway, little space is dedicated in literature about team composition, considering their personal inclinations towards design tasks.

For these 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

TeamworkDesign educationResearch methodologies and methodsData AnalysisDesign Team Composition
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 91 - 100
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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