Immersive Quantified Learning Lab
We explore the learning worlds of the future
Sensors allow us to gain insight into emotional states and motor processes that are invisible to outsiders. Does a person feel comfortable in a learning situation or does the learning environment induce stress? Is the task set too difficult? In order to be able to measure such states, the Immersive Quantified Learning Lab (iQL) makes use of a variety of sensors.
The Immersive Quantified Learning Lab (iQL) tests potentials of AI for digital education and new research approaches for interactive teaching and learning methods. The iQL offers a cyber-physical learning environment with interactive communication media.
Teaching-learning content, cognitive behavior, emotional states and motor activity are recorded synchronously from sensors. This allows us to analyze individual preferences, model teaching-learning processes, and optimize teaching-learning concepts.
We combine deep learning methods with intelligent analytics to provide individual learning support for every age group. In this way, we create learning worlds that reduce cognitive load and increase efficiency.
iQL Equipment
The Immersive Quantified Learning Lab (iQL) demonstrates how sensor technologies can be used in learning environments and work scenarios to measure the cognitive state of subjects.
The iQL features the latest sensor technologies:
- Smartglasses with acceleration, tilt and electro-ocular sensors.
- Eye trackers for high-precision measurement of eye movements
- Augmented reality glasses
- Chair pressure sensors to analyze sitting position and behavior
- Smart wristbands that measure hand movement, heartbeat and electro-dermal activity
- Infrared cameras that measure facial and especially nasal temperature
iQL Goals
- Development of sensor-based analysis methods for learning situations:
Analysis using eye-tracking, thermal imaging cameras, instrumented chairs and smartwatches, smart desks, and 3D projections of virtual objects (Hololens).
Multiple smart desks can be combined into large displays to solve problems cooperatively. - Creating learning worlds that reduce cognitive load and increase efficiency:
Based on sensor data, AI methods are used to propose measures for individual support in order to adapt learning worlds to individual needs and optimize person-specific learning. - Designing interactive, connected and participatory learning worlds:
Exploiting our everyday environment with mobile media familiar from everyday life and by implementing cutting edge technologies.
Contact
Dr. Nicolas Großmann
Phone: +49 631 20575 5304
nicolas.grossmann@dfki.de
Deutsches Forschungszentrum für Künstliche Intelligenz GmbH (DFKI)
Research Department Smart Data & Knowledge Services
Trippstadter Str. 122
67663 Kaiserslautern
Germany