- Research spotlight interviews
Research spotlight interviews
Research spotlight interview
Dr. Mirjana Sekicki
December 6, 2022
Prof. Dr. Jochen Kuhn is the chair of physics education at LMU Munich, Germany. His research interests currently focus on learning with multiple representations in STEM education and physics education through multimedia-based learning environments and technologies (smartphones, tablets, AR/VR, etc.). This includes STEM teacher training, learning with and about artificial intelligence (AI) in schools and universities, and eye tracking based examination of learning and problem-solving processes.
Physics is still mostly perceived by many students today as artificial and not very relevant to the everyday world. The content is considered "dry" and abstract, with many relationships and processes purely theoretical and invisible.
We develop and investigate approaches to bridge these tensions between abstract and phenomenological, as well as every day and discipline-specific, through the use of multimedia learning environments. By using multimedia visualizations, so-called multiple external representations, students learn with and about cognitive tools that they also need in everyday life (e.g., interpretations of diagrams and graphs) to enable them to build these bridges themselves. To this end, we also implement modern technologies in learning environments to enhance students’ (and teachers’) education with and about such media. The reason for this is that we can expect modern technologies to be used in our everyday lives in the future.
Of course, we want to find out how and with which representations students successfully learn physics, design experiments, or solve physical problems. So, we are not only interested in the outcome of learning or problem solving but in the process itself because it allows us to better understand how we can support, encourage, or challenge students to learn successfully.
There are many ways to design good physics lessons. However, learning with multiple representations offers comprehensive, static and dynamic options through the use of multimedia, and innovative learning technologies. These options will continue to increase - for education and research - especially in the wake of digitization.
So, the multiple options and the importance for social development were causes for the choice of learning with multiple representations using innovative, cutting-edge learning technologies.
When you consider that it took more than a decade, especially in Germany, for an everyday medium like a tablet to find its way more or less systematically into the classroom, we need new opportunities for future developments.
And politics need to create frameworks and programs to enable such cutting-edge developments and research of them in close cooperation with the schools inside the classrooms.
On the one hand, it is important to involve all institutions focused on education and research in such a process. On the other hand, teachers also need to be trained to implement new approaches and technologies in their classrooms in a meaningful and targeted manner.
In addition to head-mounted-displays (HMDs) for augmented reality (AR) and virtual reality (VR), we estimate eye-tracking-based systems, among others, will be part of or implemented in such next-generation educational technologies (EDTech).
For example, when learning, problem solving, or experimenting with multiple representations, we use eye tracking systems of different types (stationary, mobile, integrated into VR/AR HMD) to investigate learners' visual strategies to distinguish between successful and unsuccessful learning strategies.
Again, it is important to train and develop teachers to use such gaze-based systems.
First, one should comprehensively consider what role eye tracking analyses should play in one's research.
Becker, S., Küchemann, S., Klein, P., Lichtenberger, A. & Kuhn, J. (2022). Gaze patterns enhance response prediction: More than correct or incorrect. Physical Review Physics Education Research, 18(020107).
Dzsotjan, D., Ludwig-Petsch, K., Mukhametov, S., Ishimaru, S., Küchemann, S., & Kuhn, J. (2021). The Predictive Power of Eye-Tracking Data in an Interactive AR Learning Environment. Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, September 2021, 467–471
Klein, P., Becker, S., Küchemann, S., & Kuhn, J. (2021). Test of understanding graphs in kinematics: Item objectives confirmed by clustering eye movement transitions. Physical Review Physics Education Research, 17(1), 013102.
Kumari. N., Ruf, V., Mukhametov, S., Schmidt, A., Kuhn, J., & Küchemann, S. (2021). Mobile Eye-Tracking Data Analysis Using Object Detection via YOLOv4. Sensors, 21(22), 7668. MDPI AG.
In this series of interviews, esteemed researchers discuss how they have used eye tracking across a broad range of applications.
Eye tracking research advocate, Tobii
I work closely with scientific researchers who use eye tracking in their work. My mission is to create an ever stronger bond between the worlds of science and technology, for the advancement of our collective knowledge and wellbeing.
Dr. Stefan Küchemann shows how mobile eye tracking extends the range of possible eye tracking applications in physics education research and demonstrate underlying visual strategies during the generation process of visual representations and during experimentation.
Dr. Stefan Küchemann demonstrates two scenarios which aim to reveal the visual attention distribution and interactions of students in real-world learning environment in physics with a high ecological validity.
Dr. Jessica VandenPlas explores how eye tracking research is helping to provide a better understanding of how students learn chemistry concepts and improve methods of education.