This course is following up Service Learning I, respectively is aiming at the implementation of the project concepts, which were developed in Service Learning I. Students are encouraged to bring their own Service Learning project to life in the Austrian context. Based on the learning outcomes of the prior phases of project development and related stakeholder feedback, students will face the opportunities of a transdisciplinary learning environment, tackle “real life problems” of interaction between science and society.

• Real-life experience in a transdisciplinary environment
• Students’ involvement in the regional community
• Critical understanding and holistic perspective on the concept of socio-ecological transformation
• Students not only learn from the experience of the service alone, but by reflection on and creating meaning from the experience
• Individual personality development
• Communication and group skills
• Complexity awareness, problem analysis, critical thinking and cognitive development

Attendance at the semester Kickoff in March and at the final presentation in June is mandatory. In addition, attendance at at least 80% of the coaching sessions is a prerequisite for positive completion of the course.

The course will offer an improved understanding of socio-ecological transformation in its economic, ecological, social, cultural and political sphere in practice. It offers students the opportunity to understand different perspectives, and barriers of sustainability via an experience-based learning approach/service learning method.

Class attendance and participation (30%)
Group preparation and ability to respond to questions and engage in discussions (30%)
Final presentation (10%)
Continue to keep an individual updated service-learning research diary (30%) 

SEEP courses do not allow creation of assignments, exam answers or other assessed work using generative AI (e.g. ChatGPT).  All such work is expected to be the original work by the student concerned and is assessed as such.  Work copied from a generative AI source is equivalent to plagiarism and will be treated as such.

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