Big data techniques for skills anticipation

Policy development
Practical measure/Initiative
Spain
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Timeline
  • 2017Implementation
  • 2018Implementation
  • 2019Implementation
  • 2020Discontinued
ID number
28691

Description

What/How/Who/For whom/When of the policy development in detail, explaining its activities and annual progress, main actors and target groups.

The State Foundation for Training in Employment (Fundación Estatal para la Formación en el Empleo, Fundae) is testing big data techniques to improve skills anticipation and use them to design public training programmes in the short, medium and long term for the entire labour force.

A software tool is being developed to classify automatically the training actions (TTAA) that companies offer to their employees. To do this, automatic classification algorithms based on text mining are used to compare the textual information of the training actions, classifying them by adding an indicator of the accuracy of the task. It incorporates a novelty score for each TTAA that facilitates the identification of emerging trends in learning content. This functionality, in the development phase, is intended to allow adding new categories to be taken into account in future classifications of TTAA (automatic learning).

2017
Implementation
2018
Implementation
2019
Implementation

The new tender for the next step of this project after the first pilot experience was published in November 2018 and the contract ended in February 2020. Fundae is reviewing the product to check its compliance with the specifications and the feasibility of its implementation in the foundation's procedures.

2020
Discontinued

The tool developed for the classification of training actions is up and running and in use. The change of policies and strategies is driving skills anticipation through other paths.

Bodies responsible

This section lists main bodies that are responsible for the implementation of the policy development or for its specific parts or activities, as indicated in the regulatory acts. The responsibilities are usually explained in its description.
  • State Foundation for Training in Employment (Fundae) until 2022

Target groups

Those who are positively and directly affected by the measures of the policy development; those on the list are specifically defined in the EU VET policy documents. A policy development can be addressed to one or several target groups.

Learners

  • Young people (15-29 years old)
  • Adult learners

Entities providing VET

  • VET providers (all kinds)

Thematic categories

Thematic categories capture main aspects of the decision-making and operation of national VET and LLL systems. These broad areas represent key elements that all VET and LLL systems have to different extents and in different combinations, and which come into focus depending on the EU and national priorities. Thematic categories are further divided into thematic sub-categories. Based on their description, policy developments can be assigned to one or several thematic categories.

Governance of VET and lifelong learning

This thematic category looks at existing legal frameworks providing for strategic, operational – including quality assurance – and financing arrangements for VET and lifelong learning (LLL). It examines how VET and LLL-related policies are placed in broad national socioeconomic contexts and coordinate with other strategies and policies, such as economic, social and employment, growth and innovation, recovery and resilience.

This thematic category covers partnerships and collaboration networks of VET stakeholders – especially the social partners – to shape and implement VET in a country, including looking at how their roles and responsibilities for VET at national, regional and local levels are shared and distributed, ensuring an appropriate degree of autonomy for VET providers to adapt their offer.

The thematic category also includes efforts to create national, regional and sectoral skills intelligence systems (skills anticipation and graduate tracking) and using skills intelligence for making decisions about VET and LLL on quality, inclusiveness and flexibility.

Establishing and developing skills intelligence systems

High-quality and timely skills intelligence is a powerful policy tool, helping improve economic competitiveness and fostering social progress and equality through the provision of targeted skills training to all citizens (Cedefop, 2020). Skills intelligence is the outcome of an expert-driven process of identifying, analysing, synthesising and presenting quantitative and/or qualitative skills and labour market information. Skills intelligence draws on data from multiple sources, such as graduate tracking systems, skills anticipation mechanisms, including at sectoral and regional levels. Actions related to establishing and developing such systems fall under this thematic sub-category.

Subsystem

Part of the vocational education and training and lifelong learning systems the policy development applies to.
CVET

Country

Spain

Type of development

Policy developments are divided into three types: strategy/action plan; regulation/legislation; and practical measure/initiative.
Practical measure/Initiative
Cite as

Cedefop, & ReferNet. (2025). Big data techniques for skills anticipation: Spain. In Cedefop, & ReferNet. (2025). Timeline of VET policies in Europe (2024 update) [Online tool].

https://www.cedefop.europa.eu/bg/tools/timeline-vet-policies-europe/search/28691