Human Computer Interaction, Design and User Experience

Master's programme(s):
Course code(s):

EBE06, -, -

Course type:

Elective, Compulsory, Elective


2, 2, 2

Learning outcomes:

On completing the course, the student will be able to:

  1. Understanding the principles of human cognition, perception, and emotional states
  2. Conducting Usability/UX Testing and Reporting
  3. Designing static and interactive Prototypes of website and mobile interfaces
  4. Collecting implicit user feedback through Face Tracking tools and techniques
  5. Conducting User Research through popular UX research methodologies
  6. Analyzing qualitative data of user feedback
General competences:
  • Search for, analysis and synthesis of data and information, with the use of the necessary technology
  • Decision Making
  • Teamwork
  • Production of free, creative, and inductive thinking

This course aims to teach the fundamentals of Human Computer Interaction and UX Design focusing on:

  • human cognitive and psychological aspects that UX designers should be aware of when designing new products;
  • user research methodologies to gather and analyze qualitative and/or quantitative data;
  • tools and principles to design interactive product/system prototypes.

The topics include:

  • Web Accessibility
  • UX Elements & Usability Testing/Reporting
  • Prototyping for mobile interfaces
  • Affective Computing and Neuromarketing
  • Face & Eye Tracking Applications
  • Qualitative User Research Methods

Students will also be engaged in an academic task on a selected HCI subfield (e.g., Game-Based Learning/ Gamification, VR/AR, FaceTracking, Eye Tracking, etc.) to gain deeper understanding on the topic and develop the academic skills to perform a compete scientific research report.

The course will include open end discussions on today’s HCI trends, and hands-on workshops on: i) Prototyping, ii) Face Tracking Research, iii) User research tasks for essential data retrieval (interviews, focus groups, observation, etc.), and iv) Qualitative data analysis.

Full course outline (PDF):