Syllabus for Human-Computer Interaction: Measuring and Analysing User Experience

Learning outcomes

Regarding knowledge and understanding, on completion of the course, the student is expected to be able to:

• define the concepts 'usability', 'user experience' and 'usability problem',
• give an account of commonly occurring data collection and data analysis methods for usability studies and data analytics projects,
• describe ethical guidelines for studies that concern people.

Regarding competence and skills, on completion of the course, the student is expected to be able to:

• independently plan, carry out and analyse a usability study and a data analytics project,
• communicate results of usability studies and data analytics projects.

Regarding judgement and approach, on completion of the course, the student is expected to be able to:

• choose and justify evaluation methods under different conditions,
• reflect on benefits and risks associated with the choice of evaluation method for user experience,
• evaluate how insights gained from evaluation and data analytics projects can influence the design process when developing interactive systems,
• reflect on ethical aspects of the rights and protection of privacy of participating test persons,
• critically reflect on the use of large datasets combined with quantitative analytical methods during the design and development process of interactive systems.

Content

The aim is that the student should learn about commonly occurring quantitative evaluation methods that are used to measure and analyse user experience.

The course focuses on two main themes: i) how measurements are used in usability studies to demonstrate improvements in usability and user experience, and ii) how one can interpret patterns found in large datasets in order to bring a design process forward.

The first theme treats methods where data is generated through usability studies in a controlled environment. Data collection methods treated in this theme include questionnaires, eyetracking and observations. Analytical methods treated include descriptive statistics and different means of hypothesis testing (Chi-Square, t-test, ANOVA etc).

The second theme treats methods where large datasets are collected from many users in lifelike conditions (data analytics). Data collection and analytical methods treated in this theme include A/B testing, web analytics and basic statistical methods including descriptive statistics and hypothesis testing.

The course concludes with a reflexive and critical evaluation of the use of large datasets combined with data analytics methods in design processes related to interactive systems.

Instruction

Lectures, seminars, group work and supervision.

Assessment

The course is assessed through seminars, written assignments and examination.

If there are special reasons for doing so, an examiner may make an exception from the method of assessment indicated and allow a student to be assessed by another method. An example of special reasons might be a certificate regarding special pedagogical support from the University's disability coordinator or a decision by the department's working group for study matters.