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Module 4: Scientific and Engineering Methods and Reasoning
Last Updated: 2026-02-05 16:30:17
Abstract
In this module, we build our understanding of scientific and engineering methods and scientific reasoning. We learn a set of key scientific and engineering methods for the context of DRRS and practice them in relation to the individual Quest. We consciously explore the regenerative potential of generative AI and the potential negative feedback loops and systemic side effects.
Objective
We acquire a basic skill set of scientific and engineering methods and gain sufficient practice to be able to self-advance in further practicing those and other scientific methods and apply them to real-world challenges, i.e., one’s own Quest - to a level of understanding the method sufficiently for its usage and value, to be able to study and practice it further oneself, or through specific follow-up courses. We do not expect or claim a full proficiency level after this course, but you will know enough to be able to employ these methods in context. We learn about quantitative and qualitative scientific methods and basic engineering design methods, which we practice to a level of self-organized further learning. A specific goal is to learn about and with AI in a conscious-critical manner. Through all modules, the course integrates three high-level domains of learning competencies—cognitive (knowledge-based), affective (emotion-based), and psychomotor (action-based). In other words, the course integrates science and engineering with designerly techniques and approaches through systems thinking and sensing, building metacognition as of self- and process-awareness, relating these through embodied practices to place-specific real-world challenges in complex systems, accompanying the learning process with an inner development lens — interconnected with the individual Quest projects of the participants. The rapidly developing applications of AI with positive and potentially critical impacts and side effects are intrinsic part of the learning goals, as is the integration of “warm” data, such as intuition. The learning objective assessment starts with the preceding MOOC and its final multiple-choice quiz. To pass the MOOC, 70 percent of the questions must be answered correctly across all modules. During the CAS, active attendance in the live sessions with experts is required for each module. In addition, the Quest’s progress is monitored continuously in the peer-learning process and through individual discussions with the lecturers. Students are asked to contribute at least once per week during the course to the DRRS virtual community on Mighty Networks with internal-public sharing, commenting, or liking. The final learning and progress assessment step is submitting a Quest delivery, which - through all three DRRS CAS’ - builds the base for the Master design thesis, for those taking the full MAS in Regenerative Systems program.
Content
We study and practice scientific and engineering methods to describe, analyze, and quantify systems properties. What MOOC#3 introduces is expanded and deepened here with practical exercises. After a recap of philosophy of science, we select a set of quantitative and qualitative methods that have shown to be of direct value in building a scientific base and argumentation stock when describing and analyzing systems in the context of designerly enacting them. In specific, we will learn to work with specific quantitative methods, i.e. 1. a carbon equivalent footprint analysis to quantify the climate footprint of a product or a process and to show carbon flows in a system; 2. a life cycle analysis (lca) to quantify the environmental footprint of a product or a process and to show various flows in a system; 3. a circularity assessment of a product-process relation with five types of circular flows; 4. a social network analysis (SNA) to quantify network metrics and assess a social system for its resilience (building upon MOOC/CAS#2); 5. (ecological) sampling by, i.e., observation and grid-counting of species distribution; 6. basic statistical analysis of data, such as from the previous sampling; 7. large language models (LLM), generative AI, its possibilities, and critical aspects. We will work with selected qualitative methods from the social sciences, i.e. 8. developing a social survey as a semi-structured interview with online tools; 9. content analysis of texts, audio, and visuals with specific software; 10. focus groups as guided group discussions; 11. participatory modeling; 12. transdisciplinarity. We will explore selected engineering (-design) methods in the context of systemic design, i.e. 13. geographic information system (GIS) tools to work with spatial data and develop layered maps as a basis, e.g., for bioregional mapping; 14. point cloud development to compute basic 3D models of a product, a room, and a landscape site - using a basic lidar (laser) scanner for a product in product design and for a room in a building as the basis for architectural design; 15. point cloud development through drone flight protocols, using photogrammetry software to develop a terrain model such as for permaculture planning or simply detailed local map development for visualization and planning of local landscaping intervention; 16. Computer-Aided Design (CAD) as a method of Engineering Design - digitally drawing a basic 3D object - such as a cube - as a technical design method while considering structural constraints; 17. 3D printing (with bio-based filament) based on the point cloud development of the previous step - for a product, for a terrain, as an illustrative communication object. Some of these methods will be demonstrated in person during the field design trip to Mallorca, where we can collect data, such as the drone flight protocol, lidar scanning, 3D printing, focus group, and ecological sampling, and serve as a basis for further virtual practice after the field design trip.
General Information
- Language
- English
- Levels
- WBZ
- Frequency
- Every two years
Examination
- Type
- ungraded semester performance
Registration & Places
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Module 4: Scientific and Engineering Methods and Reasoning | No time listed | 28 h semesterly |
Offered In
-
CAS in Regenerative Systems: Systemic Design (Offered only in the Autumn Semester (two-yearly).)