The extended title of our course is Quantitative and Qualitative Methods in User-Centered Research. Academic researchers, policymakers, and designers of technologies often need to conduct user-centered experiments to capture people's attitudes, concerns, expectations, and practices toward their designed tools and policies. Conducting effective, unbiased, and reliable user studies requires in-depth knowledge of empirical research methods and analysis tools. This course serves as an introduction to a wide range of user-centered research study design methods and data analysis techniques that students can use to capture users' perceived and real attitudes and behaviors toward technologies and policies.
Below are some of the topics that are covered in the course:
Due to the significance of data privacy and security, the reading materials, examples, topics, and exams throughout the course will be motivated by usable privacy and security themes, including phishing attacks and training, privacy notice and choice, and inclusivity in security and privacy.
The course involves a final research project, where students are expected to work in small groups to conduct a privacy-related user study using the empirical methods they learn throughout the course. In addition, the course will include weekly reading commentaries, a midterm exam, and a few homework assignments. The reading commentaries are designed to introduce students to human-centered experiments and analysis methods that are commonly used in human-computer interaction and usable security research communities.
This course is suitable for students who are interested in designing and exploring inclusive tools and policies for users. At the end of the course, students will learn several quantitative and qualitative user research and analysis methods, which they can use to effectively capture users' understanding, attitudes, and practices, thereby informing the design of emerging technologies and policies. Although there are no hard prerequisites, the course is most suitable for students who have some programming, algorithms, and data analysis background (e.g., an undergraduate computer programming course, such as CompSci 101 or 201). In addition, having statistical knowledge and experience using statistical analysis tools, including R, Python, and STATA, is encouraged but not required.
At a high level, this course follows the structure below:
We work together to achieve the following objectives in this course:
Instructor: Pardis Emami-Naeini (she/her)
Class Location: Gross Hall 103
Class Time: Tuesdays and Thursdays at 3:05 p.m. - 4:20 p.m.
Office Hours: Thursdays after the class at 4:20 p.m. - 5 p.m. (If this time slot does not work for you, please email the instructor to schedule a different time to chat.)
Office Hours Location: Gross Hall 103
Resources: Course schedule, Course syllabus, Canvas, Ed, Recordings
Reading Commentaries: 10%
Discussion Lead: 10%
Active Class Participation: 20%
Group Research Project: 40%
The instructor deeply cares about the students' assessments to improve the class both for the current students, and the future ones. To this end, the instructor will ask students to complete a short survey several times during the semester. The survey will be anonymous, and no identifying information will be collected.
This course is important, but the students' continued health and happiness are far more valuable. More than anything, the instructor expects students to take care of themselves by learning what works for them. For some of us, that means taking some time from our days for meditation, exercise, or talking to a therapist. The form of self-care is not important as long as we commit to it. The instructor will try their best to be flexible, and will always be available to hear from students.