Smart Data & Knowledge Services

Individual learning is seen as the key to a modern and inclusive society, but today's learning environments only consider the individual and adaptive learning processes required for this to a very limited extent. In order to be able to remedy these problems with the help of artificial intelligence methods, individual learning must first be made quantifiable. This is the topic of Immersive Quantified Learning.

We investigate how state-of-the-art sensor technology, such as eye tracking, thermal imaging  or electrooculography, can be used to measure and record the cognitive states of learners during learning processes. Based on this, artificial intelligence methods are used to draw conclusions about individual preferences, emotional states or under/overload from this data. With the help of this data, learning/teaching processes will now be individually adapted to reduce cognitive load and optimize learning behavior. With the help of modern technology such as augmented and virtual reality, phenomena that are invisible to learners (such as electric current or thermal radiation) are also made vividly visible, immersion is increased and understanding of these phenomena is improved.

Together with the working group “Physics Teaching Research Group” of the University of Kaiserslautern (), DFKI Kaiserslautern operates the iQL – Immersive Quantified Learning Lab, where the corresponding concepts are directly explored in cooperation between computer scientists and didacticians and implemented in a classroom scenario.

To this end, we pursue the following topics, among others:

• Recording of human teaching/learning behavior through sensor technology
• Analysis of learning/teaching behavior using methods of artificial intelligence
• Testing of a wide variety of sensor technology for use in learning scenarios
• Generation of individual learning paths and individual adaptation of the teaching material to the test persons
• Visualization of invisible phenomena by means of virtual and augmented reality
• Application of the collected findings in a classroom scenario

Selected projects:

• Immersive Quantified Learning Lab – iQL (together with TUK)
• HyperMind – The Anticipating Textbook (BMBF as part of U.EDU)
• Intention-based Cooperative Robot Action Planning and Worker Assistance in Factory Environments – InCoRAP (BMBF)
• ESPEDUCA (JST CREST)