Research Center for Learning Technologies

The term Learning Technologies refers to the collection, aggregation, analysis, and evaluation of data about learners and their digitally supported learning contexts. This form of data analysis is motivated by technical, pedagogical, political and economic considerations. The evaluation of learner data makes it possible to assess the effectiveness of digital learning formats and methods in order to better understand and optimize the learning process as a whole. Digital education enables personalized support for learners, which the German University of Digital Science can use directly to improve its offerings.

In recent years, various forms of digital learning have emerged that can be categorized as lifelong learning. MOOCs – Massive Open Online Courses – have been particularly successful. These are offered by renowned universities worldwide on various platforms. The trend is to bundle such courses into packages in order to obtain so-called micro-credentials or micro-degrees. As a native digital university, the German University of Digital Science is predestined to analyze different digital learning opportunities in a comprehensive way, to research quality criteria for good teaching and to set scientifically based quality standards.

The Research Center Learning Technologies will address these challenges, especially in the following areas:

  • Learning analytics for improving (digital) teaching
  • Use of AI methods in teaching, assistance systems
  • Online examination formats, online support
  • Competency-based formats, project-based learning, teamwork
  • Game-based learning, support of the learning process through playful elements
  • Automatically assessable tasks, programming, math
  • Learning on mobile devices
  • Establishing international quality standards

The research center Learning Technologies involves professors and researchers (computer science, AI, distributed systems, statistics, pedagogy, didactics, sociology, law) and companies in an interdisciplinary way with other research centers of the German University of Digital Science. In addition, collaboration with other universities and industry through research partnerships or external Ph.D. students is planned.

Due to the overall digital character of the German University of Digital Science, doctoral and master students are not bound to a specific location. Through regular communication with supervisors and active (weekly) participation in the Research School of the Research Center, students and doctoral candidates learn to interact and learn from each other. Topics for master’s theses and doctoral dissertations are derived from the research foci and projects.

Research Groups:

  • Educational Technologies and Social Learning
  • Multimodal Learning Technologies
  • Digital Education and Internet Technologies

Members of the Research Center:

The following Professors and Senior Researchers from the German UDS, along with their scientific collaborators and PhD students, are affiliated with the Research Center for Learning Technologies

Explore Our Other Research Centers

Discover more innovative research taking place at German UDS

Digital Transformations

Digital Transformations should take into account all aspects mentioned in the concepts of e-Science, e-Infrastructures, Open Science and Science 2.0 as issues to be considered in policy making. The different terms refer to the development of new ways of conducting scientific activities using ICT, incorporating previous themes and adding a new layer. Digital Transformationsis the new growth of science and research resulting from all the existing and new, constantly evolving possibilities offered by communication networks, the digital availability of scientific content and new activities and interactions enabled by technology.

Extended Reality

The Research Center for Extended Reality at German UDS is a pioneering institution dedicated to exploring the vast possibilities of Virtual and Augmented Reality (VR/AR) technologies. As digital environments become increasingly integral to everyday life, Extended Reality is at the forefront of research and innovation, striving to unlock the full potential of immersive technologies for a wide range of applications, from education and healthcare to entertainment and industrial training.

Cybersecurity

The Research Center for Cybersecurity at German UDS focuses on three primary areas: Security Awareness, Economics of Security and Advanced Security analytics. Security Awareness addresses the human element of cybersecurity. Recognizing that technology alone cannot mitigate all threats, we work on innovative intelligence services and comprehensive education and training programs tailored for various audiences, from executives to regular end users. Economics of Security examines institutions and incentives in cyberspace. At the Research Center we analyze regulations that shape the market for cyber services and products, and look at the incentives of different groups of attackers. We develop a method for measuring the cost of an attack and to measure the impact of protection. Advanced Security Analytics leverages big data, machine learning, and AI to identify, detect, and respond cyber threats. By analyzing vast amounts of data in real time, we aim to detect threat that traditional security approaches often miss, providing a proactive defense mechanism against evolving and sophisticated cyber-attacks. At the Research Center for Cybersecurity, we are dedicated to researching and developing modern and advanced security approaches through innovative research, collaboration, and education, aiming to create a secure digital world.


Artificial Intelligence

Artificial Intelligence has gone from being an exotic subject of research to a driving force in the development of IT applications over the past 30 years. The focus of research is on what is known as machine learning, i.e., the computer is supposed to discover the solution strategy for a wide variety of problems from large amounts of data on its own. This is why today’s AI is developing hand-in-hand with strategies for storing and making available massive amounts of data. Today, this is known as big data, i.e., data that is no longer collected locally, but in the cloud around the world. Deep learning is therefore much more than the use of multi-layer neural networks. Deep learning requires combining all of the above to create adaptive systems that can understand and translate language, or even drive autonomous vehicles.