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The Chair of Educational Data Science, headed by Prof. Dr. Sven Hilbert, deals with data science in education. The teaching focus is the methodological training and further education of students of psychology, educational science and the teaching profession. Students and doctoral candidates acquire competence in independent scientific work with methods from the fields of Data Science, especially Machine Learning, and Statistics.
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NEWS - BOOK 'MACHINE LEARNING'

The book Machine Learning – Eine Einführung für Psychologie, Geistes- und Sozialwissenschaften (german)?by Sven Hilbert, Elisabeth Kraus, and Alfred Lindl has been published by Springer as an eBook and softcover edition. It provides an accessible introduction to the methods and applications of machine learning — especially for those aiming to explore data-driven questions in research, academic studies, or professional practice. The authors explain key concepts, typical fields of application, and the advantages of machine learning over traditional statistical approaches. Step by step, readers learn how to prepare data for learning processes, adjust models, and interpret results — with practical guidance using the R programming language.

In the area of research, the Chair of Educational Data Science develops new quantitative methods and deals with the methodological implementation and evaluation of educational and classroom-related projects.

Supervision of final theses

If you are interested in bachelor and master theses with methodological orientation, please contact Prof. Sven Hilbert. Current topics to be assigned:

• Overfitting of structural equation models

If you are interested in admission theses and final theses on school-related issues, please contact Dr. Alfred Lindl.