Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
Ability to plan and execute empirical user studies with correct scientific methodology. Skills in inferential statistics, modeling, computational data analysis, and visualisation. Understanding the connection between the phases of empirical research in human computer interaction: design problem, research question, hypothesis, measurement, analysis, and inference. Understanding how user research relates to user interface design via user modelling and data driven-design.
Schedule: 07.09.2020 - 13.10.2020
Teacher in charge (valid 01.08.2020-31.07.2022): Jussi Jokinen, Antti Oulasvirta
Teacher in charge (applies in this implementation): Jussi Jokinen, Antti Oulasvirta
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Basics on how to conduct scientific user studies, analyse the resulting data, and utilise the results in design. The focus is on statistical inference and computational tools of user research. The course covers study design (e.g., experiments, A/B testing), data analysis and statistical inference, user modelling, and data-driven design. While the lectures cover the distinct phases of user research, the exercises apply the lecture contents with real-life problems and data. Most exercises will be done with R.
Assessment Methods and Criteria
Lectures, assignments, exam
Contact hours 50 h
Independent learning 60 h
Lecture notes, assignments, and provided reading material
1) A previous course on human-computer interaction, interaction design, or human factors. 2) Basics in descriptive and inferential statistics. 3) Familiarity with Matlab, R, or Python.