User experience

lcomu2812  2026-2027  Louvain-la-Neuve

User experience
5.00 credits
30.0 h
Q1
Teacher(s)
Language
French
Main themes
·         Foundations and definitions of the user experience
·         User experience measures
·         User experience evaluation methods
·         Planning, data analysis and presentation of results
·         Integration of the user experience evaluation process into the development of interactive systems
Learning outcomes

At the end of this learning unit, the student is able to :

1. List and define the conceptual elements and metrics of the user experience ;
 
2. Distinguish user experience evaluation methods in terms of purpose (goal), objectives (means to reach goal), type of collected data, and deliverables ;
 
3. Compare several methods, select the most efficient, argue the choice ;
 
4. Plan and conduct the evaluation of an interactive system and propose solutions improving the user experience with this system.
 
Evaluation methods
Evaluation is based on continuous assessment, with no examination session.
  • Continuous assessment: individual written knowledge test (40%) and project (60%), comprising an individual component (20%) and a group component (40%).
  • September session: oral examination. If the project is failed, an individual tailored assignment must also be submitted on the first day of the session.
Artificial intelligence (AI): The use of AI tools is governed by the rules set out in the faculty guidelines on this subject, available on the faculty intranet, in the information provided to students.
  • Permitted without disclosure: language assistance; exploration and ideation; literature research. References must have been consulted and analyzed by the students and may not be used solely on the basis of a response provided by an AI tool.
  • Permitted with disclosure: quantitative and qualitative data analysis; figure generation; generation, correction, or adaptation of computer code (Python, R, etc.). For these uses, students must indicate the tool used and provide a link to the conversation as well as the complete prompts. A non-functional link or a link that does not allow the declared use to be verified will result in a grade of 0 for the work concerned.
  • Prohibited: simulation of participants; generation of data instead of actual data collection; and, more generally, any use of AI that would prevent the assessment of the student’s own knowledge, skills, and work process.
Faculty or entity


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] in Communication

Master [120] in Information and Communication Science and Technology