Vol. 36 No. 3 (2024)
Articles

How to improve employability in engi- neering students: A study about student career planning and perception of labor market

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  • Elena Barzizza,
  • Nicolò Biasetton,
  • Riccardo Ceccato,
  • Monica Fedeli,
  • Concetta Tino
Elena Barzizza
Department of Management Engineering, University of Padova, Vicenza, Italy
Nicolò Biasetton
Department of Management Engineering, University of Padova, Vicenza, Italy
Riccardo Ceccato
Department of Management Engineering, University of Padova, Vicenza, Italy
Monica Fedeli
Department of Philosophy, Sociology, Pedagogy and Applied Psychology, Univer- sity of Padova, Padova, Italy
Concetta Tino
Department of Philosophy, Sociology, Pedagogy and Applied Psychology, Univer- sity of Padova, Padova, Italy
DOI: https://doi.org/10.26398/IJAS.302

Published 2024-12-24

Keywords

  • Employability,
  • Engineering,
  • Machine learning,
  • Labor market,
  • Career planning

How to Cite

Barzizza, E., Biasetton, N., Ceccato, R., Fedeli, M., & Tino, C. (2024). How to improve employability in engi- neering students: A study about student career planning and perception of labor market. Italian Journal of Applied Statistics, 36(3), 327–352. https://doi.org/10.26398/IJAS.302

Copyright (c) 2024 Barzizza, E., Biasetton, N., Ceccato, R., Fedeli, M., Tino, C.

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Socio-economic factors such as unemployment among young adults have influenced graduates’ perception of the labor market and their career planning. In this context, education plays a central role and in particular there is the need to understand which are the most important soft skills and competences that universities should promote to enhance employability among students. The aim of this paper, therefore, is to investigate engineering student perception of labor market and career planning. Data was collected by means of a questionnaire administered to engineering students of the University of Padova and analyzed by applying suitable machine learning models to investigate the relationship between student perception of the labor market and career planning, and some other factors, i.e. general information, self-perceived employability, career proactivity, and career control. Results of the analysis and their educational and social implications are presented and discussed.

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