Introduction to Educational Technology and Learning Analytics 
We are teaching an elective course open to any TUM student - this is the first offering of its kind at TUM. The course serves as a gentle introduction to educational technology and learning analytics - the buzz words you may have heard around new tools and data that enters classrooms in secondary schools, university, and workplaces. Just what is the history behind these tools? What kinds are there? What do we know about their effectiveness? Should they support adaptive curriculum? Or adaptive assessment? Or is it about scaling peer interactions? What about AI in education? The course will help you understand this landscape and equip you to think critically about these things and understand 'where to next' in your own ambitions around EdTech and the use of data in learning and teaching.

Tutorials in Quantitative Social Sciences link to the course

This course is complementary to the earlier Introduction to Quantitative Methods in that it extends scientific reasoning in social sciences beyond statistical applications to how data and models are used. The topics are causality, measurement, prediction, discovery, probability, and uncertainty within social scientific research questions. Importantly, the content of the course (the textbook and the materials) offer conceptual introduction to these large topics. The course refreshes students understanding of statisics acquired in IQM and focuses on R programming but fully in line with the level and style of IQM and Data analysis in R offered to the students (ie complementary and focus on conceptual understanding of relevant analytical aspects and developing analytical fluency).

Advanced seminar: Dashboards in Educational Sciences: Design, Applications, Evidence
The course presents educational stakeholders with a broad view on dashboards in educational settings that are used to offer learners and teachers feedback they need to improve learning and teaching practices. The course reviews basic elements around the design, existing applications, and evidence of effectiveness of educational dashboards. The course is suitable for a wide range of educational researchers and practitioners regardless of their background in data science in education or learning analytics. 

Advanced seminar: Social network analysis in educational research link to the course
The seminar focuses on the theory and methods of quantitative network analysis in educational research.