Associate Professor, School of Information
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Designing effective interactive systems requires understanding the needs and capabilities of the people who will be using them. In this UX course we will focus on how to interact with users (or potential users) to understand what they need, what they currently do, what they love and hate, and examine human capabilities and behavior as they relate to UX design.
Learners will be introduced to numerous techniques to gather data from and about users.
This course is part of the User Experience (UX) Research and Design specialization offered on Coursera.
What you'll learn:
Find out what user needs assessments are, what qualitative research is, and how the two are related.
Learn an end-to-end methodology for qualitative research that is suited for understanding user needs. The methodology
includes knowledge of semi-structured interviews, in-situ observation, and affinity walls.
Be exposed to good practices for conducting semi-structured interviews, in-situ observation, and affinity walls.
Gain some experience with semi-structured interviews, in-situ observations, and affinity walls.
Welcome to Understanding User Needs, a hands-on course focused on qualitative research methods that support the design and redesign of products and services. Learners gain practical experience conducting interviews, observations, and affinity wall analysis to identify user needs, desires, and preferences through real-world practice. The course is part of the User Experience Research and Design series.
This abbreviated syllabus description was created with the help of AI tools and reviewed by staff. The full syllabus is available to those who enroll in the course.
Module 1: Introduction and Qualitative Research Overview
Module 2: Interview Protocols
Module 3: Interviews, Observation, and Data Extraction
Module 4: Affinity Walls and Analysis
Module 5: Conclusion
The course grade is based on five quizzes worth 25% (5% each), a module 1 project (10%), a module 2 assignment (15%), a module 3 assignment (15%), a module 4 report (20%), and a completion of peer grading (15%).
Associate Professor, School of Information
Course content developed by U-M faculty and managed by the university. Faculty titles and affiliations are updated periodically.
Beginner Level
No prior experience required