Course description
When you design a new user interface or adopt a new software for your company, how do you evaluate whether these changes improve your or your company's performance? By how much will the performance be improved? Will the changes be worth the cost? How certain are you in the answers to these questions? How can you convince your colleagues or supervisors to believe in your findings?
To address these questions, researchers and practitioners in human–computer interaction use quantitative research methods to collect data, design experiments, and analyze the results. This course introduces students to the following key methods of quantitative research:
- Choosing measurements for user experience, both objective (e.g., speed and accuracy) and subjective (e.g., perceived workload and stress) • Designing and conducting controlled experiments with proper internal and external validity
- Analyzing data both exploratory and inferential statistical analysis • Retrieving, extracting, and evaluating knowledge from the scientific literature as basis or additional evidence for your findings • Writing up your findings accurately with adequate detail for future replications
- Presenting key information in your findings convincingly to the audience that are not specialized in HCI Students will learn these methods hands-on through assignments and project work. This course is an ideal preparation for a thesis and future research work in the field of human–computer interaction.
Knowledge in this course is also essential for practitioners such as user experience specialists.
Grading
- Assignments (in teams): 40%
- Project (in teams): 60%
