Requirements

Requirements for the development of learning modules in VET 4.0

Definition: Learning module in VET 4.0 is a didactical learning unit to implement the newly emerging work processes and tasks.

The modules developed in this project deal with typical topics of digitalization and automatization in industrial production processes, in electronics and mechatronics.

On a methodological level they use the possibilities and tools the internet and cloud computing offer to enhance learning in distance and presence settings.

The modules are supposed to fulfil the following general guidelines and to consider the special recommendations to VET 4.0. Due to limited technical equipment, different previous experience in the partner countries and lacking former experience, it was not compulsory to meet all of them.

General guidelines

• Learning modules have a concrete product or a learning product (theoretical or virtual).
• The competences to be achieved are described.
• The learning modules have phases for planning, implementation, exercise and assessment and evaluation (feedback about competence increase).
• The learning product is achieved through cooperative and interdisciplinary work.
• The learning module promotes self-controlled and individual learning processes.
• It is ready for open use.
• It can be carried out independently from special software or learning material providers.
• It is transferable to other sectors.
• It can be implemented in the current curriculum or be offered as add-on for an additional qualification.
• Background information for its implementation is provided (e.g. learning arrangements and techniques, cooperation forms, possible assessment, necessary infrastructure, equipment, etc.).
• It can be assigned to an EQF-level.

Recommendations with special regard to VET 4.0

• The learning module promotes e-learning and blended-learning.
• It is based on cloud computing.
• It promotes collaborative working of different persons/groups and of human beings and machines/robots.
• It can apply advanced analytics (e.g. data-mining, big data analytics).
• It prepares for working with automated, responsive and mobile processes.
• Its development and implementation is multidisciplinary (T-shaped).
• It promotes self-organization.
• It considers data security and data protection.
• It considers social implications of VET 4.0