In Switzerland, Vocational Education and Training (VET) is organized as a dual system, with apprentices alternating between schools and their workplaces in companies. The training involves many different stakeholders, such as vocational school teachers, in-company and inter-company trainers as well as the apprentices themselves. Aligning the different blocks of the vocational training is thus a challenging task.
Web-based learning and performance documentation (LPD) tools have the potential to act as a “boundary object“, facilitating interaction and exchanges between the different learning locations (Caruso et. al 2020). However, the use of such tools in the context of vocational education is rather new and not much research has been conducted to investigate how they can support the training of apprentices.
The objective of this project is to analyze the user interaction data of apprentice chefs using a web-based LPD tool. The data was collected from the DUAL-T project in collaboration with the Swiss Federal Institute for Vocational Training and Education (SFIVET) led by Prof. Dr. Alberto Cattaneo. Throughout their formation, the apprentices used the tool to create and revise digital logs of their recipes and experiences, as well as to receive feedback from their in-company trainers.
By applying educational data mining methods, this project aims at determining specific behavior patterns that are more likely to emerge in successful learning experiences. On the other hand, the analyses may also allow to identify possible “at-risk” students, helping trainers to plan possible interventions in a timely and efficient manner. In a further step, these findings can then also be leveraged to implement systems that provide feedback in an autonomous way, further optimizing the support for both apprentices and trainers.