Materials+ : A Team-based Learning Course
Uncertainty is an inherent part of the scientific method. While new ideas are key to innovation and creativity, they always bring along risks. Materials+ has been designed to create a risk-friendly environment where students are strongly exposed to uncertainties while learning personal and social skills, technical tools, and the mindset required to tackle applied scientific problems.
To promote effective learning of key material concepts in an applied context and stimulate the development of soft skills, Materials+ offers a Team-Based Learning (TBL) environment. The course is taught in a four-phase cycle, including orientation (1 week), a readiness assurance process (3 weeks), an applied scientific project (application exercise, 10 weeks), and a peer-assessment element at the end of the course (Figure 1). Our main objective is to promote sustained learning of material science concepts by combining them with technological and multidisciplinary tools. Materials+ comprises about 30% of passive learning (readiness assurance phase), and 70% of active learning in the hands-on part (application exercise). Students learn underlying concepts during the passive mode and apply and consolidate them by solving authentic and real-world challenges.
Elements of active learning are also present in the passive phase of the course. During the orientation phase, teaching instructors present the course and ask students to discuss and write a “Team contract” — a document that lays out the expectations for how they expect to work together and how to resolve conflicts on the group level. In the readiness assurance phase, frontal lectures are prepared to expose the students to the fundamentals of the material science concepts and technological tools required to develop the applied project. At this point, we apply a performance assessment to verify whether the students are well prepared to start addressing the hands-on challenge. Such readiness assurance tests are carefully designed to contain simplified versions of the problems that the students are likely to face during Phase 3 of the course.
In Materials+, the key component of the learning process consists of open dialogue between students, coaches, and teaching instructors.
We facilitate the learning process by assigning coaches (teaching assistants) to each group.
Coaches and teaching instructors are present during contact hours throughout the semester. Digital communication channels (MS-Teams) are also established to guarantee a permanent feedback-loop of information and sufficient support. It is important to mention that, both instructors and teaching assistants do not act as lecturers in the classical sense. Our goal is to act as brainstorming mediators. Instead of simply providing solutions to the students, we guide discussions in the group and monitor the project’s progress. To subject the students to new and diverse perspectives, coaches are recruited from all degree programs of ETH (on average 50% D-MATL, 50% other Departments). Such a “coaching” system can increase the engagement of the students and make them more susceptible to take more responsibility for their own learning.
The student’s performance is assessed by three different examinations (Figure 1): in the readiness assurance phase (Phase 2), individual exams are applied to verify the knowledge acquired from the introductory frontal lectures. The grade obtained from this phase accounts for 30 percent of the total grade. During the application exercise phase (Phase 3), the rationale of the design employed by the students in each group is assessed by a committee (coaches, lecturers) in a poster presentation session and a final written report, to which the teams receive detailed feedback from the teaching instructors and have the opportunity to implement them in a revised version. In Phase 4, (peer-assessment) students assess the performance of their peers using an impartial distribution of assets. Such peer-assessment method has been successfully employed in other course settings at ETH and is based on an applied economic theory that provides a fairer method to determine the contributions of each team member.
Nowadays students are increasingly overwhelmed with the requirements of curriculum contents and expectations that extend beyond what they can effectively learn in a given timeframe. More important than learning content- and method-specific competencies, students of the 21st century also need to develop personal and social assets, such as adaptability, creative and critical thinking, decision-making, negotiation techniques, communication skills, and a more collaborative mindset. Most of these assets are incompatible with the traditional teaching techniques and can only be developed with the creation of a risk-friendly learning environment.
To engage students in a sustained learning experience and promote a strong development of personal and social competences, we create a learning environment that goes beyond the traditional teachercentered classroom activities. To maximize the learning efficiency and engagement in this environment, it is important to not only introduce active learning elements but to seek an optimal ratio between passive and active modes of learning. Finding such an optimum range requires dealing with teaching as a design science and establishing efficient communication channels with the students.
When developing our course, we aimed to create an engaging, risk-friendly learning experience. The key innovation lies in combining established pedagogical methods, encouraging students to take risks, negotiate perspectives, and materialize ideas. Our multidisciplinary, project-based structure fosters critical thinking, teamwork, and problem-solving. Students tackle real-world challenges, analyze problem spaces, synthesize perspectives, and collaborate effectively. We also introduce a user-centered design thinking strategy, emphasizing prototyping, iteration, and creativity. This approach equips students with essential skills for their future careers.
At the heart of our course, we’ve fostered a collaborative communication structure among instructors, coaches, and students. Regular feedback loops, guidance on team dynamics, and technical advice are provided from multiple perspectives. An expert automation coach supports all
teams throughout the semester. Instructors and coaches ask thought-provoking questions, guide teams through group dynamics, and advise on course concepts and project management. Students share feedback during instruction sessions via coaches or directly with instructors. Assessment prioritizes problem-solving, critical thinking, communication, and creativity. The mid-term poster presentation serves as a project milestone, helping teams converge ideas for the final sprint. While the final challenge isn’t graded, it encourages students to explore diverse solutions. Their creativity often surprises us. We enhance engagement through brainstorming sessions, regular feedback, and peer assessment. Active participation and motivation thrive within teams. This prepares students for dynamic professional careers in our information-rich world.
Lessons Learned
- Teaming up with like-minded lecturers can be greatly beneficial. It ensures diversity of ideas and increases resistance to failure during the design phase of the course.
- Find the right balance between passive and active learning for your own teaching style and according to the type and content of your course.
- Transfer to the students more responsibility for their own learning. This is a powerful strategy to promote strong engagement in the classroom.
- Find the right balance between being a coach and a teacher for your students. Encourage and guide students to search and propose answers for scientific questions themselves. Learning can be greatly improved in a risk-friendly environment, as failure is an essential part of the process.
- Be clear on expectations. Distribute performance assessments throughout the semester. Choose boundary conditions that do not impact negatively on the creative process of the students.
Project team
Professur für Nanometallurgie
Vladimir-Prelog-Weg 1-5/10
8093
Zürich
Schweiz
Complex Materials
Vladimir-Prelog-Weg 1-5/10
8093
Zürich
Schweiz