The vision of our research covers Design, Analysis, and Engineering of flexible and open learning systems and the underlying Theories, Models, Architectures, Paradigms, Algorithms, Methods and Tools to build such systems. The scope of our learning technology research spans from single components (algorithms, apps) to complex and integrated systems and is applied on all forms and levels of learning such as primary and secondary schools, higher education institutions, professional learning, informal learning, and lifelong learning.
Our research revolves around the following topics:
Our current Learning Technology research projects mainly focus on the following areas:
- Mobile Learning in Context: The widespread use of mobile technologies has led to an increasing interest in mobile learning. Context is a central topic of research in that area. In fact, a major benefit of mobile devices is that they enable learning across contexts. In our research in this field, we explore how context can deliver significant benefits in mobile learning.
- Learning Analytics: Learning Analytics (LA) deals with the development of methods and tools that harness educational data sets to support the learning process. It is a multi-disciplinary field involving machine learning, artificial intelligence, information retrieval, statistics, and visualization. In LA several related areas of research in TEL converge: These include academic analytics, action research, educational data mining, recommender systems, and personalized adaptive learning. LA focuses on the development of methods for analyzing and detecting patterns within data collected from educational settings, and leverages those methods to support the learning experience. In Technology Enhanced Learning (TEL), masses of data can be collected from different sources and kinds of student actions, such as searching, retreiving and organizing information and learning resources, solving assignments, taking exams, online social interaction, participating in discussion forums, and extracurricular activities. This data can be used for Learning Analytics to extract valuable information that might be helpful for teachers to e.g. reflect on their instructional design and management of their courses or by students to e.g. reflect on their learning.
- Open Assessment & Feedback
Our second area of research covers Computer Science Education with a focus on
- Extracurricular learning in STEM and Computer Science in particular
- Computer Science Teacher Education
- Gender & Diversity in Computer Science
For additional information please have a look at our
- current annual report (as well as previous editions)
- our publications.
If you are interested in conducting your research in Learning Technology as member of our group, the information about Doctoral Studies might be helpful for you.
Created by System Administrator. Last Modification: Wednesday, 02. May 2012 09:23:00 by .