Artificial Intelligence (AI) methods have shown to have a profound impact in educational technologies, where the great variety of tasks and data types enable us to get benefit of AI techniques in many different ways. We will review relevant methods and applications of AI in various educational technologies, and work on problem sets and projects to solve problems in education with the help of AI.

The course aims at providing students with practical knowledge and skill of processing, interpreting and analyzing empirical educational data, including different lenses through which to view the nature of inquiry in the field, research design, and an overview of quantitative, qualitative and mixed methods research.

The course aims at equipping students with a suite of advanced quantitative and qualitative tools to support their existing research and develop new lines of inquiry in the Learning Sciences. By providing opportunities to analyze empirical educational data, the course will allow students to develop an appreciation for the breadth of methods that can be employed to improve the process of learning

Failing is an inseparable part of learning. Yet, understanding how we can benefit from failing remains often unexplored. This course combines research from the learning sciences, as well as educational and cognitive psychology, to address the question of how and why we can learn from failing within the context of human learning.

How do people learn and how can we support learning? This course provides an overview of major theoretical perspectives that attempt to describe how learning works, and serves as an introduction to interpreting education as a means of designing learning environments.

How do people learn and how can we support learning? This course provides an overview of major theoretical perspectives that attempt to describe how learning works, and serves as an introduction to interpreting education as a means of designing learning environments.