Computational Competencies
Algorithmic modelling, data analysis, and simulation - computational competencies - have become the third pillar of many study programs at ETH. The Computational Competencies initiative helps teachers and study programs embed these competencies meaningfully and coherently in disciplinary learning.
Overview and goals
The Computational Competencies (Comp2) initiative is a strategic project at ETH Zurich aimed at integrating algorithmic modeling, data analysis, simulation, and programming into all Bachelor's and Master's programs. This initiative establishes "Learning Trajectories" (Lernspuren) that bridge foundational methods in computer science and mathematics with specific applications in various scientific disciplines.
The strategic goals of the inititive are:
- Curricular integration: all programmes provide tailored and coherent learning trajectories (Lernspuren) for Computational Competencies
- Foundations are introduced early and systematically and applied and deepened in disciplinary contexts.
- Computational competencies are not considered an add-on, they are an integral part of domain teaching
- Strengthen teaching: coordinate service courses, make Comp2 links explicit in domain lectures; use interactive notebooks and project?based learning
- Build structures: common competence catalogue as a reference, technical & didactic support, and sustainable processes.
For students
- Apply theory using data, models and simulations
- Develop judgement about assumptions, limits and uncertainty
- Experience computation as a discipline?specific tool, not a generic add?on
For teachers & study programmes
- Make abstract concepts concrete and testable
- Reduce redundancy and gaps between courses
- Align teaching with research practice and professional reality
What this means: Students acquire practical competencies in modelling & simulation, statistics & inference, machine learning, and programming — together with reflection on societal, legal and ethical implications. All this closely tied to and applied in their field of study.
ETH Competence Framework & Computational Competencies
Computational Competencies map onto the ETH Competence Framework. While most competencies are in the block of subject specific competencies, Comp2 can be found in all areas of the ETH Competence Framework, as illustrated by the colors:
Impact of Generative AI on Computational Competencies & Programming
Gen AI has and still is revolutionising how we produce code. Skills like critical thinking, conceptual thinking, testing and evaluation become more important than ever. While discussions about the impact of GenAI and a shift in competencies this may imply are still ongoing, the core competencies as listed above will remain important.
Embedding of Computational Competencies
Many impactful contributions are small, well?placed changes to existing courses. Typical entry points:
Low?threshold integration
? Short Jupyter?based exercises in lectures or exercises
? Visualising models or data rather than only discussing them
? Automated feedback for programming tasks
Strengthening a course
? Data?driven or simulation?based assignments
? Explicit discussion of assumptions and model limits
? Assessments that require explanation and interpretation
You do not need to teach a programming course to contribute to Comp2. Computational elements can support conceptual understanding in any discipline.
Design program?level learning trajectories
For sustainable impact, competencies should not remain isolated in individual courses. Programs define learning trajectories (Lernspuren) across semesters:
? Introduce foundations early (often in service courses)
? Apply and deepen repeatedly in disciplinary contexts
? Ensure later courses build on shared tools and assumptions
The Curriculum Information Tool (CIT) visualises where competencies are taught and assessed – making gaps and redundancies visible. Find out more!
Typical challenges
? Long gaps without computational application
? Over?reliance on single courses or individuals
? Unclear handover between service and departmental courses
JupyterHub for teaching (maintained by EduIT & UTL): Browser?based notebooks for code, data and text – no local installation for students.
- Tailored environment accessibly via Moodle
- Moodle integration for distribution, submission & grading
- Automated feedback via OtterGrader
- Git sync with nbgitpuller; optional NFS for large datasets
- Concurrent editing of a Notebook available for real-time collaboration
CodeExpert (provided by D-INFK)
- Large?scale programming teaching and exams
- Optional GPU?ready hosting for specialised courses
- Automated feedback
- standardized and scalable programming teaching
We support teachers and program teams throughout planning and implementation:
- Didactic consultation on embedding computational competencies
- Technical support for JupyterHub (EduIT/UTL) and CodeExpert (D-INFK)
- Communities of practice (user meetings, booster workshops)
-> Contact us via
Funding for innovative teaching projects is available via Innovedum.
Whether you plan to embed a small computational element, redesign a course or develop learning trajectories in the curriculum – we are here to support you.
- Update a course with computational elements
- Revise a study programme or prepare a curriculum review
- Find examples and inspiration from other departments
Get in touch via to discuss how Computational Competencies can support your teaching - in didactic, technical or curricular aspects.
Project examples
Overview of projects about compuational competencies, funded via
- Innovedum projects about Computational Competencies
Contact
ETH
UTL