Abstract:

Work-study training is a vocational training system that offers many advantages to both companies and apprentices. It allows the latter to acquire practical and theoretical skills while working in a company, and to prepare effectively for their future career.

The principle of work-study training is that the apprentice or employee on a professionalization contract spends part of his or her time in a company and part in a training center. During the period spent in the company, the apprentice works alongside professionals, learns the skills needed to do his or her job, and gains professional experience. During the period of training in the center, the apprentice follows theoretical courses that complete his practical training in the company.

This form of work-study training is managed by its stakeholders through a digital apprenticeship booklet. This digital booklet for work-study program is a digital tool that allows apprentices to follow their training online, in a simple and intuitive way. It has been compulsory since January 1, 2019, and centralizes all information relating to apprentices, enabling them to manage their career path in an efficient manner.

Although the digital booklet for work-study has many advantages, its introduction has required overcoming certain technical and organizational issues to ensure effective and secure use of the tool. Indeed, with the large amount of data collected through the digital learning logbook, the processing and organization of the data is becoming increasingly complex.

In this research we propose an approach based on semantic web technologies that allows us to improve the process of processing and organizing the collected data. To do so, we propose a method of knowledge representation based on data from a digital booklet for work-study training.
In this paper, we propose a system for knowledge extraction from collected data and knowledge representation.

Keywords:

Work-study program, education, data mining, semantic web, information system, knowledge engineering.