Online peer learning session: “Using text mining to analyse skills supply and demand”

ETF Skills Lab Network of Experts organize this online peer learning session in collaboration with our partners from the University of Pisa.

This first session will take place on 19 January from 10:00 to 12:30 CET and will focus on the use of text mining to analyse skills supply and demand, methodological approach also adopted in ASSETs+ project for technologies and skills analysis.

The event will start with a detailed presentation of the methodology behind the recent paper Are universities ready to deliver digital skills and competences? A text mining-based case study of marketing courses in Italy.

Participants will then be invited to take part in an open brainstorming session to explore how this methodology can be used in other countries and different languages.

The session is open to everyone, but participants will need to register to participate via zoom. They can also register as members of ETF (European Training Foundation) before or after the webinar, if they are interested.

Cowater International and our partner Fondazione Giacomo Brodolini are the contractors selected by  ETF.

Can higher education systems keep up with changes in labor market?

The journal “Technological Forecasting and Social Change” has published a scientific paper of our partners from the University of Pisa, where they show how natural language processing can help in finding an answer.

We still know little about the role of higher education in the digital transformation. If on one side the labour market is constantly evolving and is asking for an upskilling process of the workforce, higher education institutions struggle to be agile enough. Therefore, it is necessary to measure and to better understand this gap.

In the scientific paper “Are universities ready to deliver digital skills and competences?“, our partners from the University of Pisa: Irene Spada, Filippo Chiarello, Simone Barandoni, Gianluca Ruggi, Antonella Martini and Gualtiero Fantoni, describe a quantitative approach to deal with this problem, focusing on the marketing sector. Through text mining, they develop a model of automatic job competencies extraction from Italian texts, using it to retrieve the skills expressed both in the exam’s descriptions of universities and the job vacancies.

The results allow to compare the skills offered by higher education with the labor market’s needs in Italy, exploring and highlighting the digital gaps existing between the two.

ASSETs+ applied a similar approach in the automatic skills analysis realized during the first year of the project: we extracted skills from job vacancies using the automated algorithm presented in the paper. The automatic reading of the description allows us to detect patterns of terms similar to the ESCO skills and formalize the proposal for ESCO.

The ASSETs+ project aims to build a sustainable human resources supply chain for the European Defence Industry, that boosts innovation by both attracting highly-skilled young workers and upskilling its employees. The ASSETs+ main goal is to design and develop education and training programmes, from their prototyping to replication in other contexts,  to provide trainees with new skills and knowledge related to key technologies that will be expected in the Defence sector in the coming years.


ARTICLE: Towards ESCO 4.0 – Is the European classification of skills in line with Industry 4.0? A text mining approach

We share with you this interesting article in which our partners from the University of Pisa and Fondazione Giacomo Brodolini have participated as co-authors.

ESCO is a multilingual classification of Skills, Competences, Qualifications, and Occupations created by the European Commission to improve the supply of information on skills demand in the labour market. It is designed to assist individuals, employers, universities and training providers by giving them up to date and standardized information on skills.

Rapid technological change means that ESCO needs to be updated in a timely manner. Evidence is presented here of how text-mining techniques can be applied to the analysis of data on emerging skill needs arising from Industry 4.0 to ensure that ESCO provides information which is current.

The alignment between ESCO and Industry 4.0 technological trends is analysed. Using text mining techniques, information is extracted on Industry 4.0 technologies from: two versions of ESCO (v1.0 – v1.1.); and from the 4.0 related scientific literature. These are then compared to identify potential data gaps in ESCO .

The findings demonstrate that text mining applied on scientific literature to extract technology trends, can help policy makers to provide more up-to-date labour market intelligence.

Authors: Filippo Chiarello, Gualtiero Fantoni, Terence Hogarth, Vito Giordano, Liga Baltina, Irene Spada