Focal point themes
You can choose from the following topics for your Innovedum focus project proposal:
Digitalisation of Course Units
The increased digitalisation of course units continues the blended learning approach, in which traditional face-to-face teaching is combined with modern online learning methods. Various digitalisation approaches make learning content accessible in a flexible and location-independent way, providing students with a more personalised and adaptable learning experience. This method not only promotes students' independence and personal responsibility, but also helps lecturers to make their teaching materials more efficient and interactive. In particular, course units with large numbers of students can benefit from digitalisation in order to reach and support all students equally.
Digitising course units also makes it possible to update and expand learning resources in real time. This ensures that students always have access to the latest information and developments in their field of study. The integration of multimedia elements and interactive tools can also be used to make complex topics clearer and easier to understand. Educational media can be developed in collaboration between lecturers, students and the Media & Methods Lab (UTL) or Multimedia Production (ID). An important goal of these collaborations is to create sustainable conditions so that the media can be (further) developed independently in the future.
For more Information please visit our website Digitalisation of Course Units.
Project Inspirations:
- Digital learning platforms such as Moodle support personal learning processes through a combination of self-learning and in-class activities. This is facilitated by a variety of digital elements such as interactive learning materials, online exercises, discussion forums and various planning functions.
- Students apply theoretical knowledge directly in simulations, such as those that can be created with JupyterHub, and thus gain practical experience.
- Students are immersed in interactive environments that facilitate the understanding of complex concepts (e.g. through the use of virtual or extended reality).
- Simulated experiments provide access to expensive or dangerous materials and facilitate safe and flexible work.
- Students benefit from automated feedback and adaptive questions in self-tests that support their individualised learning. They work on challenging questions in mathematics and computer science in self-study, e.g. by integrating CodeExpert or STACK into a Moodle course. This relieves the pressure on exercise hours.
- Authentic digital exam settings and self-tests enable students to demonstrate and deepen their knowledge and skills in realistic situations.
AI in Teaching and Learning
The integration of AI in education presents a number of opportunities and challenges for both students and lecturers. By effectively harnessing AI-powered tools and technologies, educators can create more personalised and engaging learning experiences for students, while also preparing them for the AI-driven world that awaits them. First and foremost, project applications are wanted that deal with the integration and application of existing AI tools in teaching.
Teaching with AI
By utilising existing AI-powered tools, educators can create personalised learning experiences for students, streamline course design and content creation, and provide students with personalised feedback and support. This can lead to improved student engagement, academic performance, and a more efficient use of study time.
Learning with AI
AI-based and particularly generative AI tools can offer students the opportunity to solve challenges in a project-based, interdisciplinary setting, enabling them to gain AI-specific competencies and transferable skills in the application of AI with respect to ethics, data (protection) law, and social impact.
Learning about AI
AI is a rapidly evolving field, and it is important for students to develop the necessary critical thinking and problem-solving skills to thrive in an AI-driven world. AI-based tools can help students learn about AI in a hands-on way, by solving real-world problems and applying AI concepts to real-world data.
For more Information please refer to the FAQ on our Webpage regarding AI and Education.
Competency-based Teaching
Competency-based teaching focuses on students not only acquiring knowledge but also being able to apply it in real-life situations. Teaching aims to foster independent learning and problem-solving skills by using practical tasks and active learning methods. A learning-outcome-oriented approach to teaching supports this by clearly defining the skills and knowledge to be acquired, facilitating planning and making learning success measurable.
In addition to the focus on skills acquisition in specific subject areas, this approach promotes basic skills and interdisciplinary skills. These include computational competencies as well as social, methodological, and personal skills. This means that students not only acquire subject-specific knowledge but also develop basic skills in areas such as algorithms, data analysis, and artificial intelligence. In addition, competencies in teamwork, critical thinking and problem solving are fostered, which are essential for their personal and professional development.
The focus topic “Competency-based Teaching” supports innovative didactic and technology-supported projects that emphasise the acquisition of competencies and link skills and knowledge across different areas.
Further information:
- ETH Competence Framework
- ETH Computational Competencies: JupyterHub
- Download Formulating competence‐oriented learning objectives (PDF, 101 KB)
Examples of projects:
- A project- and team-based learning environment that specifically addresses adaptability and reflects on how students deal with change.
- Integration of programming tasks in subject teaching to link computational competencies with the application subject, e.g. with the help of JupyterNotebooks/JupyterHub, CodeExpert, etc.
- Development of peer feedback learning opportunities for students and associated peer grading.
- Use of performance assessments to determine whether a person has critically and creatively engaged with a problem.
- Application of methods and techniques for processing and analyzing subject-specific data (observations, laboratory measurements, sample data sets, etc.) , e.g. with JupyterNotebooks, etc.
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Further suggestions
- Examples of implementation from ETH teaching: Competence View
