Proposing a Machine Learning Approach to Analyze and Predict Employment and its Factors
- Francisco J. García-Peñalvo 123
- Juan Cruz-Benito 123
- Martín Martín-González 4
- Andrea Vázquez-Ingelmo 123
- José Carlos Sánchez-Prieto 13
- Roberto Therón 125
- 1 GRIAL Research Group. University of Salamanca
- 2 Department of Computer Science. University of Salamanca
- 3 Research Institute for Educational Sciences. University of Salamanca
- 4 UNESCO Chair in University Management and Policy. Technical University of Madrid
- 5 VisUSAL Research Group. University of Salamanca
ISSN: 1989-1660
Año de publicación: 2018
Volumen: 5
Número: 2
Páginas: 39-45
Tipo: Artículo
Otras publicaciones en: IJIMAI
Objetivos de desarrollo sostenible
Resumen
This paper presents an original study with the aim of propose and test a machine learning approach to research about employability and employment. To understand how the graduates get employed, researchers propose to build predictive models using machine learning algorithms, extracting after that the most relevant factors that describe the model and employing further analysis techniques like clustering to get deeper insights. To test the proposal, is presented a case study that involves data from the Spanish Observatory for Employability and Employment (OEEU). Using data from this project (information about 3000 students), has been built predictive models that define how these students get a job after finalizing their degrees. The results obtained in this case study are very promising, and encourage authors to refine the process and validate it in further research.