The Human Computer Interaction programme (HCI) has a duration of two years and a total size of 120 European Credits (EC). The curriculum consists of 80 EC of course work and a 40 EC Thesis Project.

Curriculum

  • Compulsory courses (35 EC)
  • Primary Electives (15 EC)
  • Secondary Electives (30 EC)*
  • Research part (40 EC)

*To expand the thematic range of your master's programme, you can replace the secondary electives by an interdisciplinary profile.

Study programme Human Computer Interaction
Our learning takes place in the context of real-world societal and research challenges. This allows our students to directly impact both enterprises and the scientific community.
Judith Masthoff - Human Computer Interaction

Educational methods

  • lectures
  • work groups
  • practicals
  • projects
  • self-study

Examinations

  • Exams
  • Assignments
  • Participation in practicals
  • Projects
  • Products

Interdisciplinary profiles (30 EC)

A profile is a coherent set of courses which can be chosen to expand the thematic range of your Master's programme. The courses are on a single theme that is usually not a standard part of the programme. You can choose from three different profiles:

Educational profile – teacher degree

If you are passionate about sharing your knowledge, and you would consider a career as a teacher in secondary education, this profile might be right for you. The emphasis of the Educational profile is on practitioner skills and school-based activities. Throughout the profile, learning theories and teaching methods will be taught closely linked to your day to day work in the classroom. The profile is tailored to meet the professional development needs of teachers in the early stages of their careers. More information.

Applied Data Science profile

Applied Data Science is a multidisciplinary profile for students who want to better understand and be able to apply Data Science methods, techniques, and processes to solve real-world problems in various application domains. The foundations of applied data science include relevant statistical methods, machine learning techniques and script programming. Moreover, key aspects and implications of ethics, privacy and law are covered as well. Data science methods and techniques are investigated using case studies and applications throughout the life sciences & health, social sciences, geosciences, and the humanities. More information.