The "Smart Learning in Online Further Training" (SLOW) project continued the predecessor project "Smart Learning in the Skilled Trades" with a new focus. The follow-up project further developed the technical components as well as new self-learning and blended learning scenarios for initial and further training in energy technology, business administration and trades in general. Practical partners then trialled various learning scenarios based on digital learning content. In the case of self-learning without classroom guidance, effective and targeted knowledge acquisition is supported by the learning analytics software.

The project focused on the implementation and testing of a purely online course concept and the joint use of the Smart Learning infrastructure by different institutions. For this purpose, new online content and courses were created and the necessary technical adaptations for three educational institutions were integrated.

Together with the Berlin-based Fachinstitut für Informatik und Grafikdesign (FiGD), the IZT developed an online continuing education course as a preparatory course in accounting. This was tested at the three educational institutions FiGD, the Berlin Chamber of Crafts (HWK) and Vocational School 1 Uelzen (BBS1). In addition, further energy courses were created by the IZT for the HWK and BBS 1. Only with the creation of extensive digital media was it possible to meet the needs of the target groups for learning content that could be used flexibly. On the technical side, the learning recommendations were optimised for use in online courses and new types of learning paths were developed so that the individual learning progress of the learners can be experienced and understood within the framework of a self-learning concept even without a lecturer. Gamification elements and a communication tool were created to improve the motivation of the learners. The lecturers also benefit from an improvement of the editors. The work was evaluated by the IZT during the course implementation in the institutions and the results were made widely accessible to the public.

This project was supported by the BMBF under the funding code 01PD17002C.