Introduction to User Research and Design

An Open Educational Resource

Augusta University Professional Writing and Rhetoric Program

Supported by an Affordable Learning Georgia Transformation Grant

Course Website: www.uxdtextbook.com

Version 1.0; 2026

Front Matter

Introduction to User Research and Design

About This Resource

This open educational resource (OER) introduces user research and design for students in professional writing, rhetoric, and related fields. It was created through the Augusta University Professional Writing and Rhetoric (PWR) program with support from an Affordable Learning Georgia (ALG) Transformation Grant. The resource supports four core PWR courses—ENGL 2680: Professional and Technical Writing, ENGL 3585: Digital Rhetoric, ENGL 3687: User Research and Design, and ENGL 3700: Introduction to Professional Writing and Rhetoric—and replaces commercial textbooks at no cost to students.

A Student-Co-Created Text

This is not a conventional textbook. It was co-created by faculty and students working as a tiered contributor team: a faculty project lead, a graduate research assistant, and two undergraduate assistants and contributors who researched, drafted, and revised chapters, concept entries, and resource annotations. Contributor names appear on the chapters and entries they authored. Because the resource was built this way, chapters and entries retain the voice of their authors rather than being homogenized into a single editorial register. This is deliberate: a text about centering real people is more honest when the people who made it remain visible in it. Resource annotations, in particular, are written in the contributors' own reflective first person. The co-creation model serves a second purpose: students learn user research and design partly by producing a usable and public resource about it, and the text stays grounded in the experiences of the students it serves.

Acknowledgments

Development of this resource was funded by an Affordable Learning Georgia Transformation Grant (Round 27, Grant 756) The project team thanks the University System of Georgia and Affordable Learning Georgia; the Augusta University Libraries; and the students of ENGL 3687, ENGL 4690 whose interactions and feedback shaped these materials.

License

Except where otherwise noted, this resource is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to copy, redistribute, remix, transform, and build upon the material for any purpose, including commercially, provided you give appropriate credit, link to the license, and indicate if changes were made. Embedded third-party materials (such as linked videos) remain under their own licenses and are not covered by this license.

Suggested Citation

Kays, T. M., Pothineedu, S., Broadnax, A., & McGriff, A. (2026). Introduction to user research and design. Augusta University / Affordable Learning Georgia. https://uxdtextbook.com/. Licensed under CC BY 4.0.

Accessibility

This resource is designed to meet WCAG 2.1 AA standards.

Reporting Errors and Suggesting Improvements

This is a living document. To report an error or suggest an improvement, contact Trent M. Kays, PhD (tkays@augusta.edu).

How to Use This Resource

If You Are a Student

Start with the Introductory Chapters, which give you the history, contexts, and ethical foundations of user experience work. Then move into the Main Chapters, each of which teaches one research method—observing users, interviewing, surveys, usability testing, and more—with step-by-step guidance you can apply immediately in coursework. When you hit an unfamiliar idea, check the Concepts section: short, plain-language entries that define key ideas and point you to related ones. Every method in this book can be practiced with free tools and the resources already available either on campus or online.

If You Are an Instructor

Chapters are modular and do not assume sequential reading; assign them in the order that fits your course. Each chapter includes learning outcomes, discussion questions, and references that can anchor assignments. Because the resource is licensed CC BY 4.0, you may rearrange, excerpt, or adapt any portion—including for courses outside professional writing—provided you attribute the original. The chapters map to the four Augusta University PWR courses listed in the front matter, but nothing in them is institution-specific except the examples.

If You Want to Adapt or Contribute

Adaptation requires no permission—only attribution. If you teach at Augusta University and want to contribute a chapter, concept entry, or resource annotation, contact the project lead; contributor guidelines and templates are available on the project site: www.uxdtextbook.com. Contributions from students are reviewed and co-developed with faculty before publication.

How the Resource Is Organized

• Introductory Chapters: the history, contexts, and ethics that frame user research and design.
• Main Chapters: one research method per chapter, written to be practiced, not just read.
• Concepts: a cross-referenced glossary of key ideas in accessible language.
• Helpful Resources: annotated academic and industry sources for going deeper.

Introductory ChaptersHistory and Background of UX

Main Chapter Contributor: Aryn Broadnax

Supplemental Chapter Contributor: Trent M. Kays, PhD

Chapter Description

This chapter addresses the history and background of User Experience from ancient times to modern day. Learning the history of UX offers insight into how the field can help others and where it may go in the future.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Describe the evolution of user-centered design, from ancient practices to the rise of technology and modern UX
2. Identify key milestones and concepts and explain their contributions to User Experience
3. Analyze how the historical development of UX provides insight into the future direction of the field

Main Chapter Text

Feng Shui and Ergonomics

To understand the background of User Experience (UX), we must look at its history. The historical foundation of UX is often traced back thousands of years to ancient China and the philosophy of Feng Shui (风水), which translates literally as “wind-water.” Some popular accounts date the practice as early as 4000 BC, though precise dating is contested among historians. The practice dictates the arrangement of objects in a space to create a harmonious balance, influencing human well-being. Feng Shui had the goal of promoting health and happiness and is a model for the concept of a user-friendly experience (Stevens, 2021).

The evolution of user-centered design continues in Ancient Greece, only a few thousand years later. Beginning in the 5th century BC, Greek tools show evidence of ergonomic design. Ergonomics, the science of designing and arranging objects to maximize productivity and minimize discomfort, mirrors the goal of Feng Shui. A classic example is found in the writings of Hippocrates, who was often called the father of medicine. His writings described the optimal workplace setup a surgeon should have, such as the lighting in the room, the surgeon’s position, and the arrangement of tools, which he advised should be placed so as not to obstruct the surgeon while remaining within easy reach when required (as described in Stevens, 2021). Hippocrates reminds many UX designers of issues they may need to consider when deciding how to create products for users.

Video resource: How Architects Use Feng Shui To Design Famous Buildings [YouTube, 8:18 minutes, captions available]

The Early and Mid-Twentieth Century

A few thousand years later, mechanical engineer Frederick Winslow Taylor pioneered Taylorism, which is also known as Scientific Management. In his 1911 book, The Principles of Scientific Management, he suggested that in order to solve inefficiency, managers needed to develop systematic management, attempting to optimize the connection between humans and their tools. Even though Taylorism is widely criticized for reducing humans to mere machines, this concept anticipated some key UX principles (Vinney, 2023).

In the late 1940s, Bell Labs hired John E. Karlin, an industrial psychologist widely regarded as the father of human-factors engineering in American industry. Karlin persuaded Bell Labs to establish the first human-factors department at any U.S. company in 1947 and became its director in 1951. His group’s research, which was grounded in systematic studies of speed, accuracy, and user preference, shaped the design of the touchtone telephone keypad introduced in 1963, a layout that remains the standard today on phones, ATMs, keypads, and medical equipment (Fox, 2013).

Some commentators reach even further back for a “first UX designer” and nominate Walt Disney. Dickerson (2013) argues that Disney’s relentless attention to the guest experience (observing visitors, fixing what did not work, taking risks, surrounding himself with talented people, and continuously “plussing” his parks) anticipated core UX practices such as user observation, iteration, and feedback loops decades before the digital age. The claim is best read as an argument about mindset rather than a literal title, but it illustrates that user-experience thinking predates the technologies we now associate with it.

Late 1900s: The Rise of the Tech Era

In the 1970s, technology entered a new phase and the foundation for personal computing was laid. Many psychologists and engineers created the groundwork for user experience, working at Xerox’s PARC Research Center. They helped create the mouse, the graphical user interface, and computer-generated bitmap graphics. PARC’s work later inspired the development of the Apple Macintosh, which was Apple’s first commercial PC featuring a graphical user-interface, mouse, and built-in screen (Stevens, 2021).

This commercial success resulted in computers going mainstream, with Apple becoming a giant in UX technology, especially with the rise of the iPod and iPhone in the early 2000s. The term “user experience” itself was coined in the early 1990s by Donald Norman, a cognitive scientist who joined Apple as a User Experience Architect, which was reportedly the first job title to use the phrase. Norman chose the term deliberately broadly, explaining, “I wanted to cover all aspects of the person’s experience with a system, including industrial design, graphics, the interface, the physical interaction, and the manual” (Vinney, 2023). It was also in this decade that Jakob Nielsen made his actual mark on the field where he popularized fast and low-cost “discount” usability methods and, in 1998, co-founded the influential consultancy Nielsen Norman Group with Norman.

UX in the Present Day

In present day, UX is constantly changing and more UX design jobs have appeared. With the creation of more technological apps and inventions, there are brand new interaction design concepts suited for what designers are designing for. UX is now also shifting from smartphones, computers, and websites to something more dynamic, such as artificial intelligence, virtual reality, voice technology and more. In the future, UX will grow and develop with the times, crafting new experiences for users, and the potential demand for UX designers will grow with it.

Questions to Consider

1. We learned that Feng Shui is about harmony and flow. How can you use your technological environment, such as your phone’s home screen or computer’s desktop, to apply Feng Shui? What would you move around and change so it’s less stressful and more pleasing?
2. Hippocrates taught us that a surgeon’s tools need to be easy to reach. Think about the size of your phone screen and the navigation of your applications. Are the buttons on your favorite apps easy to hit with your thumb, or do you have to stretch and maneuver to reach them?

References

Dickerson, J. (2013, September 9). Walt Disney: The world’s first UX designer. UX Magazine. https://uxmag.com/articles/walt-disney-the-worlds-first-ux-designer

Fox, M. (2013, February 8). John E. Karlin, who led the way to all-digit dialing, dies at 94. The New York Times. https://www.nytimes.com/2013/02/09/business/john-e-karlin-who-led-the-way-to-all-digit-dialing-dies-at-94.html

Stevens, E. (2021, July 28). The fascinating history of UX design: A definitive history. CareerFoundry. https://careerfoundry.com/en/blog/ux-design/the-fascinating-history-of-ux-design-a-definitive-timeline/

Vinney, C. (2023, January 9). The complete history of UX (user experience). UX Design Institute. https://www.uxdesigninstitute.com/blog/history-of-ux/

Contexts and Approaches to UX

Main Chapter Contributor: Aryn Broadnax

Supplemental Chapter Contributor: Trent M. Kays, PhD

Chapter Description

This chapter explores the critical role that real-world contexts play in user-centered design and the practical approaches UX professionals use to address them. Understanding the context in which a product is used, including physical, social, legal, and technical, is paramount to successful design, and field studies are essential for understanding users’ actual attention levels and secondary activities in their natural environments. Designers rely on structured frameworks, such as the four-step human-centered design cycle, and tailor experiences to device type: “checking” behavior on mobile, “immersing” on a tablet, and “managing” tasks on a desktop.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Analyze how environmental, social, and physical contexts influence user interactions with a product
2. Identify the legal, ethical, and accessibility factors, such as accessibility law, data-privacy regulation, and age restrictions, that constrain design solutions in a given context
3. Select appropriate UX methodologies, such as User-Centered Design, based on specific project goals

Main Chapter Text

Contexts of UX

A central priority in user-centered design is understanding the specific context in which a product will be used. For technology, this involves a variety of factors, including the physical environment, social setting, and the abilities or disabilities of the user. Essentially, context describes the real-world circumstances of use. While not every factor applies to every situation, it is critical to identify which ones are relevant (Interaction Design Foundation, n.d.-a).

For example, almost all products must meet legal standards regarding accessibility. Other regulations may govern data privacy (such as the GDPR), age restrictions, or local gambling laws. Furthermore, while the physical environment may not affect a standard website, it is vital for technology used outside of an office. Examples include outdoor ATMs, agricultural equipment that requires steam cleaning, or inventory systems used in freezing warehouses.

Approaches to UX

The first step of UX is to understand the context of use. To integrate this understanding into the final product, designers must also adopt structured approaches. Relying on assumptions in a design studio is rarely sufficient because context is multifaceted. Instead, UX professionals utilize formal frameworks, observational research, and device-specific strategies to ensure their designs align with real-world scenarios.

The Human-Computer Interaction (HCI) Framework

Usability is inextricably linked to context in the realm of user-centered design. The international standard for human-system interaction defines usability based on how well specified users can achieve goals efficiently within a specified context of use (International Organization for Standardization [ISO], 2018). Designers generally follow a four-step approach to implement this:

• Understand and specify the context: Identify the technical, physical, and socio-cultural environments (e.g., hardware, workspace, and cultural norms)
• Specify user requirements: Determine what the user needs based on that specific context
• Produce design solutions: Create prototypes or systems that directly address these contextual boundaries
• Evaluate the designs: Test the solutions within the actual or simulated context to ensure they perform effectively under real constraints

Designers rely on observational approaches rather than isolated brainstorming to accurately define these contexts. Field studies and contextual inquiries are essential methods. By going to the spaces where users actually engage with the product, designers can observe physical limitations, social pressures, and environmental disruptions firsthand (Interaction Design Foundation, n.d.-b).

Device Intent and Mobile Contexts

Approaches to UX must also adapt to the technology being used, particularly distinguishing between mobile and desktop experiences. Mobile UX requires a specialized approach because users operate within overlapping spheres of context. They’re constantly balancing their personal goals against intermittent attention levels, device constraints (like screen size or network drops), and shifting physical surroundings.

Consequently, modern UX approaches often categorize devices by user intent rather than just physical location. Devices are viewed through a subjective, purpose-driven lens: mobile phones are primarily for checking (short, intermittent tasks like searching for something or scrolling through social media), tablets are for immersing (leisure or reading), and desktops are for managing (complex, sustained workflows). By designing for intentional context rather than just objective location, UX professionals can tailor experiences that mimic the analog tools that we have in our daily lives.

Questions to Consider

1. Think about a mobile app you use every single day. How would your experience change if you had to use it outside in glaring sunlight, or while carrying heavy groceries in one hand?
2. The text suggests that mobile phones are for “checking” and desktops are for “managing.” What is a specific task you always save for your computer because trying to do it on your phone is too frustrating, and why do you think that is?

References

Interaction Design Foundation. (n.d.-a). Contexts of use. https://www.interaction-design.org/literature/topics/contexts-of-use

Interaction Design Foundation. (n.d.-b). The interaction design process. https://www.interaction-design.org/literature/topics/interaction-design-process

International Organization for Standardization. (2018). Ergonomics of human-system interaction—Part 11: Usability: Definitions and concepts (ISO 9241-11:2018). https://www.iso.org/standard/63500.html

The Ethics of User Thinking

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter examines the ethical obligations that come with studying people, such as the consent, confidentiality, and power considerations that make user research trustworthy rather than merely effective. Readers will develop the judgment to recognize when research that is legal, convenient, or technically impressive is nonetheless ethically deficient, and frameworks for acting on that recognition under real-world pressure.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Explain the foundational principles of research ethics, including informed consent, confidentiality, and the harm/avoid harm obligation, and their origins in the Belmont Report.
2. Analyze cases where legally permissible research practices fail ethical scrutiny, and articulate why compliance and ethics are not the same standard.
3. Apply professional frameworks, including the ACM Code of Ethics and design justice principles, to evaluate the ethical dimensions of a proposed user research study.

Main Chapter Text

A Study That Was Legal

In 2014, researchers at Facebook published the results of an experiment conducted on 689,003 of the platform’s users. For one week, the company had algorithmically altered what those users saw in their news feeds, including reducing positive posts for some and negative posts for others, to test whether emotional states spread through networks. They do: users shown less positivity wrote measurably more negative posts of their own (Kramer et al., 2014). No participant knew they were in an experiment. No one consented beyond the platform’s general terms of service. The study was, by the company’s account, legal. The public response from users, ethicists, and regulators made clear that legality had not been the question.

This episode illustrates a fundamental tension at the center of user research and design: the methods that generate the most ecologically valid and behaviorally grounded data are often the methods that place researchers in the most ethically complicated territory. Observation, testing, and experimentation are powerful precisely because they capture what users actually do rather than what they say they do, but that power comes with obligations. Understanding those obligations, and developing the judgment to act on them under real-world pressure, is what this chapter is about.

The Foundations: Consent, Confidentiality, and Do No Harm

Ethical user research rests on three interlocking principles that have roots in medical and social-science research ethics: informed consent, confidentiality, and the obligation to avoid harm. Informed consent means that participants understand what they are agreeing to before a study begins and not merely that they have clicked an “I Agree” button, but that they have received a clear and jargon-free explanation of what the research involves, what data will be collected, how it will be used, and what they can expect during and after participation. Confidentiality means that identifying information is protected, that participants’ responses cannot be traced back to them without their permission, and that data is stored and disposed of responsibly. The harm principle—drawn most directly from the Belmont Report (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979), the foundational document of research ethics in the United States—requires researchers to weigh potential benefits of a study against potential costs to participants, and to design research in ways that minimize risk.

In formal academic settings, these principles are implemented through Institutional Review Board (IRB) processes, which review research protocols before data collection begins. But most user research in professional and commercial contexts does not go through IRB review, which means researchers must internalize these principles and apply them through judgment rather than bureaucratic compliance. The absence of required oversight does not reduce ethical obligation.

The Compliance Gap: Why Legal Is Not Ethical

The Facebook study cleared every legal requirement that applied to it, which is exactly what makes it instructive. Terms-of-service consent is consent in only the thinnest sense: no reasonable user reads such agreements as authorization for psychological experimentation. The gap between what rules permit and what respect for persons requires is where most real ethical failures in user research occur and not in dramatic violations but, rather, in practices that are routine, legal, and quietly corrosive. These are dark patterns that engineer agreement, data collection that exceeds any stated purpose, and A/B tests whose subjects would object if asked. Research on deceptive design practice has documented how easily practitioners slide into these patterns when business incentives and ethical reflection point in different directions (Gray et al., 2018). The professional habit this chapter argues for is simple to state and hard to keep: treat “is it allowed?” as the beginning of the question and never the end.

Power, Positionality, and Vulnerable Populations

Research is a relationship with a power gradient. The researcher chooses the questions, frames the tasks, interprets the silences, and writes the report; the participant, however respected, is the studied party. That gradient steepens with vulnerable populations, like children, patients, employees observed by people their managers hired, and users dependent on a service they cannot leave, and it bends findings when researchers fail to notice whom their methods exclude. A recruiting pipeline that reaches only the convenient produces research about the convenient. Design justice scholarship pushes this point further: communities affected by a design should participate in shaping it and not merely be studied for it (Costanza-Chock, 2020). For student researchers, the practical discipline is positional honesty—asking, before every study, who holds power in this exchange, who is missing from it, and whose interests the findings will actually serve.

A Vignette: The Campus Navigation App

Consider a student team building a campus navigation app. To improve routing, they propose logging users’ movements continuously in the background. The data would be anonymized, the feature disclosed in the privacy policy, and the legal review clean. Now apply the chapter’s questions. Would users, asked plainly, agree to continuous location tracking by classmates? Whose movements does such a dataset expose most and consider what it reveals about a student who visits the counseling center, the food pantry, or the disability resource office. Does “anonymized” survive the re-identification risk of location traces, among the most re-identifiable data that exist? The team’s eventual design (opt-in route logging, on-device processing, no retained traces) cost them analytic richness and bought them something more valuable: a product their own participants would endorse if shown exactly how it works. That trade is the ethics of user thinking in miniature.

Practical Frameworks for Everyday Judgment

Principles need handles. The ACM Code of Ethics and Professional Conduct (Association for Computing Machinery, 2018) offers one set: contribute to society, avoid harm, be honest, respect privacy, which are all commitments written for practitioners who will face these questions without an IRB in the room. Design justice offers another, reframing “users” as communities with standing rather than data sources (Costanza-Chock, 2020). And for daily work, a durable heuristic: imagine explaining your study, in full detail, to the people in it and not the sanitized recruiting blurb. You explain the real design, the real data flows, and the real beneficiaries. If that conversation would embarrass you, the study needs to change before it runs. Ethical research is not research that never raises hard questions; it is research whose authors asked them first.

Questions to Consider

1. The Facebook emotional contagion study was legal. Identify three specific points at which it failed the principles in this chapter, and propose what an ethical version of the same research question might look like.
2. Most professional UX research never passes through an IRB. Does that make practitioner research less ethically constrained, or more dependent on individual judgment? Defend your answer.
3. Apply the “explain it to the people in it” heuristic to a data practice of an app you use daily. Would the explanation survive? What would have to change?
4. In the campus navigation vignette, the ethical design was analytically poorer than the original proposal. When, if ever, is it acceptable for a team to accept worse data for better ethics — and who should make that call?

References

Association for Computing Machinery. (2018). ACM code of ethics and professional conduct. https://www.acm.org/code-of-ethics

Costanza-Chock, S. (2020). Design justice: Community-led practices to build the worlds we need. MIT Press.

Gray, C. M., Kou, Y., Battles, B., Hoggatt, J., & Toombs, A. L. (2018). The dark (patterns) side of UX design. Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 1–14. https://doi.org/10.1145/3173574.3174108

Kramer, A. D. I., Guillory, J. E., & Hancock, J. T. (2014). Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences, 111(24), 8788–8790. https://doi.org/10.1073/pnas.1320040111

National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. U.S. Department of Health, Education, and Welfare. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html

Relevant Readings

Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press. Develops the concept of contextual integrity as a framework for evaluating whether data collection respects the norms of the social context in which information was originally shared.

Martin, B., & Hanington, B. (2012). Universal methods of design: 100 ways to research complex problems, develop innovative ideas, and design effective solutions. Rockport Publishers. A practical methods reference that situates each technique within its ethical considerations; useful alongside this chapter.

Portigal, S. (2013). Interviewing users: How to uncover compelling insights. Rosenfeld Media. Treats consent and care for participants as craft skills within the interview itself, extending this chapter into method-specific practice.

Friedman, B., & Hendry, D. G. (2019). Value sensitive design: Shaping technology with moral imagination. MIT Press. A systematic approach to building human values into the design process from the start, rather than auditing for them afterward.

Main ChaptersObserving Users in Everyday Contexts

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter examines contextual observation as a foundational method in user research, exploring how researchers can gather rich, ecologically valid data by watching people interact with products and environments in their natural settings. By the end of this chapter, readers will understand why what users do and what users say they do are often quite different and how skilled observation bridges that gap.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Distinguish contextual observation from laboratory-based and self-report research methods, and articulate the specific kinds of insights each approach produces.
2. Plan and conduct a basic observational session using established techniques, including contextual inquiry, shadowing, and naturalistic observation, while applying appropriate ethical guidelines.
3. Produce structured field notes that separate descriptive observation from interpretive inference, and use those notes to generate preliminary design insights.

Main Chapter Text

The Case for Watching

In the early 1980s, a team of researchers at Xerox PARC set out to understand why users struggled with the company’s state-of-the-art photocopiers. Rather than surveying customers or running controlled experiments, researcher Lucy Suchman and her colleagues did something deceptively simple: they watched. They filmed office workers attempting to operate the machines and then analyzed the footage in detail. What they found was striking. User behavior bore almost no resemblance to the step-by-step procedures the machines had been designed around. Workers improvised, backtracked, misread feedback, and invented their own workarounds and all in real time, shaped by interruptions, colleagues, and the particular disorder of an actual office. Suchman’s resulting analysis, published as Plans and Situated Actions (1987), became a foundational text in human-computer interaction because it demonstrated that human action is fundamentally situational: it cannot be understood in isolation from the context in which it unfolds.

This insight sits at the heart of contextual observation as a research method. Where laboratory studies isolate variables and self-report surveys ask users to reconstruct their experience after the fact, contextual observation meets users where they actually are. The goal is not to test a hypothesis under controlled conditions but to develop a textured, empirically grounded understanding of how people navigate their real environments with all the friction, improvisation, and ambient noise those environments entail.

Why Context Changes Everything

The argument for observational methods rests on a well-documented phenomenon in behavioral research: people are poor narrators of their own behavior. When asked in a post-purchase survey how they use a software tool, users describe an idealized version of their workflow, or the version they believe they should use, or the version they remember most clearly, which is rarely the one that actually caused them difficulty. This gap between recalled behavior and enacted behavior is not a matter of dishonesty; it reflects the way human memory works. Routine actions become automatic and therefore invisible to conscious reflection. Contextual observation sidesteps this limitation entirely by collecting data at the moment of action rather than in retrospect.

The implications for design are significant. A research team developing a hospital patient-intake application, for instance, might conduct in-depth interviews with nurses and learn that the current system is “generally fine.” The same team, conducting observations during a busy shift, might find nurses routinely entering duplicate data, switching between the digital system and a personal paper notebook, and sharing login credentials to save time. None of these behaviors would likely surface in an interview not because nurses are hiding them but because workarounds become invisible once they are habitual. Observation makes them visible again.

Core Observational Techniques

Researchers draw on several distinct approaches depending on their access, research questions, and the degree of interaction they wish to have with participants. Naturalistic observation involves watching behavior with minimal interference, often in fully public or semi-public settings such as transit hubs, libraries, or retail environments. The researcher’s role here is deliberately passive and the aim is to document naturally occurring behavior without shaping it. Shadowing takes a more immersive approach: the researcher follows a participant through their day or a defined work period, observing across multiple contexts and transitions rather than in a single fixed setting. This technique is especially valuable when the research interest involves sequential tasks or the way responsibilities are distributed across time and space.

Contextual inquiry, developed by Karen Holtzblatt and Hugh Beyer (1997) and refined in subsequent editions of their foundational methodology (2017), adds a structured interview layer to direct observation. In a contextual inquiry session, the researcher accompanies the participant in their natural work environment and asks questions in real time as behaviors occur: “I noticed you just switched to a different tab. What prompted that?” The master-apprentice model underlying contextual inquiry positions the user as the expert in their own experience and the researcher as a learner. This framing tends to elicit richer, more candid responses than a formal interview setting because the participant is explaining what they are already doing rather than being asked to perform or recall.

What to Observe: The AEIOU Framework

Beginning observers often feel overwhelmed by how much is happening in any given setting. One practical tool for organizing attention is the AEIOU framework, developed by researchers at the Doblin Group in the early 1990s and now a standard reference in design research (Martin & Hanington, 2012). The framework directs the observer’s eye toward five overlapping categories:

• Activities: What are people doing? What tasks are they trying to complete?
• Environments: What does the physical or digital space look like? How does it shape behavior?
• Interactions: How do people engage with objects, systems, or other people?
• Objects: What tools, devices, or artifacts are in use? How are they arranged?
• Users: Who are the people in this space? What do you notice about their roles, postures, or attitudes?

AEIOU is not a rigid checklist. It is a set of lenses. No observer captures everything at once, especially when new to fieldwork. The goal in early observation sessions is to train the eye and build the habit of noticing.

Field Notes and the Discipline of Separation

Whatever technique a researcher employs, the quality of the resulting data depends heavily on the quality of the field notes. The central discipline is maintaining a clear separation between description and interpretation. Descriptive notes capture observable facts: “Participant opened the settings menu, scrolled to the bottom, closed it without making a selection, and then used the search bar to type the word ‘notifications.’” Interpretive notes capture the researcher’s tentative readings of those facts: “Participant may not have known where to find the setting or found the menu categories unclear.” Collapsing these two registers is one of the most common errors novice observers make. If description and interpretation are merged in the moment, the raw evidence needed to reconsider a reading later, or to triangulate with other data sources, is lost.

Martin and Hanington (2012) recommend structuring field notes in two columns or clearly separated sections for precisely this reason. The discipline becomes especially important during analysis, when teams compare notes across observers and need to distinguish what was actually seen from what was inferred. Concrete, timestamped, behaviorally specific descriptions, such as “User paused for 18 seconds at the confirmation screen” rather than “User seemed uncertain”, are far more useful analytical material.

Ethics in the Field

Observation raises ethical considerations that are distinct from those governing survey or interview research. In formal research contexts, IRB approval and written informed consent are required before observing identifiable participants. In applied design research and professional settings, the obligations may differ but do not disappear. Even in public spaces where no formal consent is required, researchers are obligated to treat observation subjects with respect, protect their anonymity in notes and reports, and be transparent about their purpose if directly asked. The distinction between observation and surveillance is not merely procedural, and it reflects a fundamental orientation toward the people whose lives and labor constitute your data. Researchers who lose sight of this distinction tend to generate findings that are technically rich but ethically thin, a combination that undermines both the work and the trust of the communities it claims to serve.

From Observation to Design Insight

Raw field notes become useful research through systematic analysis. After an observation session, researchers review their notes looking for patterns: repeated behaviors, moments of hesitation or error, spontaneous workarounds, and points where the designed system and actual user behavior diverge. These divergence points are frequently the most generative findings. When a warehouse worker photographs a printed inventory sheet with her phone rather than entering the data directly into the provided tablet, the workaround is a signal it suggests that the tablet-based data entry process imposes costs (in time, awkwardness, or cognitive load) that the worker has independently calculated are not worth bearing. Identifying and understanding such signals is the core analytical task of observational research, and it is work that cannot be done from a survey or a conference room.

Questions to Consider

1. In your own words, explain why contextual observation might reveal different data than a post-use interview about the same product or task. What specific limitations of self-report methods does observation address?
2. A UX team is designing a new digital check-in system for a primary care clinic. Describe how you would set up a contextual observation session in that environment. What would you observe, how would you take notes, and what ethical steps would you take before beginning?
3. The AEIOU framework directs your attention to five categories of observation. Are there contexts where some categories would matter more than others? What would you prioritize in a campus dining hall versus a hospital waiting room versus a social media interface?
4. Consider the distinction between descriptive and interpretive field notes. Why is maintaining that separation difficult in practice, and what strategies might help a researcher preserve it during a fast-moving observation session?
5. Think about a technology you use every day — a navigation app, a learning management system, a social platform. Have you ever developed a workaround for something that didn’t work quite right? What might a researcher observing your use infer from that workaround that you might not have mentioned if simply asked?

References

Beyer, H., & Holtzblatt, K. (1997). Contextual design: Defining customer-centered systems. Morgan Kaufmann.

Holtzblatt, K., & Beyer, H. (2017). Contextual design: Design for life (2nd ed.). Morgan Kaufmann.

Martin, B., & Hanington, B. (2012). Universal methods of design: 100 ways to research complex problems, develop innovative ideas, and design effective solutions. Rockport Publishers.

Suchman, L. A. (1987). Plans and situated actions: The problem of human-machine communication. Cambridge University Press.

Relevant Readings

Goodwin, K. (2009). Designing for the digital age: How to create human-centered products and services. Wiley. Chapters 4–6 offer a thorough treatment of contextual research planning and execution in professional design settings.

Ladner, S. (2014). Practical ethnography: A guide to doing ethnography in the private sector. Left Coast Press. An accessible bridge between academic ethnographic methods and applied research in corporate and design contexts.

Norman, D. (2013). The design of everyday things (Rev. ed.). Basic Books. Provides theoretical grounding for why designed artifacts so often fail users, reinforcing the argument for observation as a corrective method.

Spinuzzi, C. (2003). Tracing genres through organizations: A sociocultural approach to information design. MIT Press. Especially relevant for professional writing students; demonstrates how observational research in workplace settings reveals the gap between designed information systems and how workers actually use them.

Conducting User Interviews

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter examines the user interview as a core qualitative method in UX research, exploring how researchers design, conduct, and analyze interviews to uncover the motivations, mental models, and unmet needs that shape how people interact with products and services. Readers will develop practical skills for planning interview protocols, asking questions that generate rich data, and translating raw conversation into actionable design insights.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Design a semi-structured interview protocol, including screener criteria, warm-up questions, and core topic areas, appropriate to a defined research question.
2. Conduct a user interview that applies active listening, appropriate probing techniques, and ethical informed-consent procedures.
3. Analyze interview transcripts or notes using affinity mapping or thematic coding to identify patterns and generate preliminary design insights.

Main Chapter Text

Why Interviews? The Limits of What You Already Know

Every designer and researcher enters a project carrying assumptions. Some of those assumptions are accurate. Many are not. The user interview exists, in large part, to surface the gap between what a team believes about its users and what is actually true. As Steve Portigal (2013) argues, interviewing is fundamentally an act of deliberate unknowing, or the willingness to set aside expertise and prior knowledge in order to hear what a specific person, in a specific context, actually experiences. Where observation shows what people do, the interview reaches what observation cannot: the reasoning, history, and meaning behind the behavior. The two methods answer different questions, which is why this book treats them as companions rather than competitors.

Interview Types and When to Use What

Interviews vary along a structure spectrum. Structured interviews ask every participant identical questions in identical order, producing clean, comparable data at the cost of flexibility. Unstructured interviews follow the participant’s lead almost entirely, producing depth at the cost of comparability. Semi-structured interviews—the workhorse of UX research—fix the topics while freeing the conversation, and most of this chapter assumes that form (Brinkmann & Kvale, 2015).

A brief vignette illustrates the difference. A research team investigating a medication-management application conducted structured interviews in their first round of research, asking every participant the same ten questions in order. The data were clean and easy to compare, but the team noticed that several participants mentioned an issue (difficulty distinguishing their own prescriptions from a family member’s) that none of the prepared questions had addressed directly. In a second round using semi-structured interviews, a single follow-up probe, “Tell me about the last time you opened the app when you weren’t the only one who might be using the device”, generated the richest data of the entire project. The structure had obscured a problem; the flexibility revealed it.

Designing a Protocol That Asks the Right Questions

An interview protocol is not a survey. Its purpose is not to generate statistically reliable responses to predetermined questions but to create a conversation structure that helps both researcher and participant explore a topic together. Good protocols are built around topics rather than questions, with two or three anchor questions per topic area and a set of potential probes, follow-up questions designed to elicit elaboration, narrative, or clarification.

The most productive interview questions share several characteristics. They are open-ended, inviting participants to construct their own answers rather than select from implicit options. They are behaviorally anchored, asking participants to recount specific past experiences rather than describe hypothetical preferences or general habits. Research consistently shows that people are poor predictors of their own future behavior, but they are often reliable narrators of specific past events (Portigal, 2013). Asking “Walk me through the last time you tried to reschedule a doctor’s appointment” will typically yield richer data than “How do you usually feel about managing healthcare appointments?”

Protocols should also include a warm-up section, or low-stakes questions about the participant’s background, routine, or general context, before moving into the core topic areas. Warm-up questions serve two functions: they build rapport and they help the researcher calibrate the participant’s communication style and level of technical vocabulary before higher-stakes questions begin.

In the Room: Listening, Probing, and Staying Curious

Conducting a good interview is a skill that improves with practice, and the central challenge is not asking questions, but, rather, it is listening. Most novice interviewers ask their prepared question, receive a response, and immediately move to the next prepared question, treating the session like a questionnaire read aloud. Expert interviewers treat each response as its own starting point. Three probe types carry most of the work: the echo probe, repeating the participant’s last phrase with rising intonation (“it just stopped working?”) to invite continuation; the expansion probe (“tell me more about that”); and the clarification probe (“when you say ‘confusing,’ what did the screen actually show?”). Silence is a fourth tool. The pause a novice rushes to fill is often the pause in which the participant decides to say the true thing. Contextual inquiry’s master–apprentice stance, in which the participant is the expert teaching the researcher their own experience, is the right posture for the whole session (Holtzblatt & Beyer, 2017).

Ethics, Consent, and Care for Participants

An interview is an intimate exchange conducted under a power imbalance, and care for the participant is part of the method, not an overlay on it. Consent must cover the conversation and, separately, any recording; participants must know they can decline any question or stop entirely without penalty; and identifying details must be protected in notes, transcripts, and reports. Interviews also wander, and a question about a scheduling app can surface a participant’s illness, grief, or fear. The researcher’s obligation in those moments is human before it is methodological: acknowledge, let the participant choose the direction, and never mine distress for data. The procedural mechanics of consent, storage, and review appear in this resource’s Research Ethics chapter; this chapter’s point is that ethics in interviewing is enacted moment to moment, in how you ask and how you listen.

From Transcript to Insight

Raw conversation becomes research through deliberate analysis. Two approaches dominate UX practice. Affinity mapping externalizes the data: discrete observations and quotations are written on individual notes, then clustered by similarity until patterns emerge from the bottom up, a method given its fullest form in contextual design (Holtzblatt & Beyer, 2017). Thematic coding works through the transcripts themselves: the researcher labels passages with codes, refines the codes across interviews, and develops the recurring ones into themes (Brinkmann & Kvale, 2015; Martin & Hanington, 2012). Both methods enforce the same discipline this book teaches for field notes, that is keep what was said separate from what you think it means, and both end the same way: with findings stated as evidence-backed claims, ready for the reporting practices covered later in this resource.

Questions to Consider

1. In your own words, what can an interview reveal that observation cannot — and what can observation reveal that an interview cannot? Give one concrete example of each.
2. Rewrite this interview question to be open-ended and behaviorally anchored: “Do you find the registration portal easy to use?”
3. The medication-app vignette shows structure obscuring a problem. When would the opposite hold — when would an unstructured conversation be the wrong choice?
4. You are interviewing a fellow student about budgeting apps, and they become visibly upset discussing debt. Walk through what you would do in the next sixty seconds, and why.
5. Take three quotations from any conversation you have had this week and affinity-map them with a partner’s three. What cluster emerged that neither of you predicted?

References

Brinkmann, S. & Kvale, S. (2015). InterViews: Learning the craft of qualitative research interviewing (3rd ed.). Sage.

Holtzblatt, K., & Beyer, H. (2017). Contextual design: Design for life (2nd ed.). Morgan Kaufmann.

Martin, B., & Hanington, B. (2012). Universal methods of design: 100 ways to research complex problems, develop innovative ideas, and design effective solutions. Rockport Publishers.

Portigal, S. (2013). Interviewing users: How to uncover compelling insights. Rosenfeld Media.

Relevant Readings

Seidman, I. (2019). Interviewing as qualitative research: A guide for researchers in education and the social sciences (5th ed.). Teachers College Press. A deeper treatment of interview structure and meaning-making from the qualitative research tradition.

Spradley, J. P. (1979). The ethnographic interview. Holt, Rinehart and Winston. The classic source for descriptive questioning and the learner’s stance that contextual inquiry later adapted.

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner’s guide to user research (2nd ed.). Morgan Kaufmann. Situates interviewing within the full toolkit of user research methods, with practical recruiting and logistics guidance.

Creating UX Surveys

Main Chapter Contributor: Srinidhi Pothineedu

Supplemental Chapter Contributor: Trent M. Kays, PhD

Chapter Description

This chapter discusses UX surveys, a structured research method used to gather user feedback on their experiences with a product, service, app, or website. It explains how surveys can help measure usability, satisfaction, and effectiveness, while providing both qualitative and quantitative data. The chapter also emphasizes the importance of thoughtful survey design, which is essential for transforming user feedback into meaningful and practical improvements.

Learning Outcomes

1. Explain the purpose of UX surveys in user research and identify when a survey is the most effective method to use
2. Distinguish quantitative from qualitative survey data and select question types appropriate to each
3. Design a short, neutral, well-sequenced survey and analyze its responses for patterns, pain points, and opportunities for improvement

Main Chapter Text

Introduction

Creating UX surveys is a research method that is used to gather structured feedback from users about their experiences with a product, service, website, or app. UX, or User Experience, surveys help researchers understand how people perceive usability, satisfaction, clarity, and overall effectiveness. Unlike informal feedback, UX surveys are intentional tools designed around a research objective. They allow teams to move beyond assumptions and collect measurable data that can inform real decisions. This method is important because design decisions based on guesswork often miss the mark. People interact with products in ways designers and developers may not realize. A well-constructed survey captures patterns across a broader audience, helping teams identify pain points, validate improvements, and prioritize changes. Surveys can give quantitative data such as satisfaction ratings and usability scores, as well as qualitative insights through open-ended responses that reveal context and emotion (Sauro & Lewis, 2016). UX surveys are especially useful after updating a system, creating a new feature, or completing a usability test. They are also helpful when one-on-one interviews are not feasible due to time or budget constraints. However, the quality of insight depends entirely on how well the survey is designed.

Step-by-Step Guide

The process of creating an effective UX survey begins with defining a clear research goal. Before drafting questions, it is critical to identify what decision the survey will inform. A vague objective, such as “collect feedback,” leads to scattered questions and unusable results. A focused objective, such as identifying why users abandon a checkout process, creates direction and purpose.

Once the goal is defined, the next step is to identify the target audience. It is important to determine whether the survey is intended for first-time users, returning users, students, mobile users, or another specific group. Mixing audiences without distinction can complicate data interpretation. Clear segmentation ensures that the feedback is relevant and meaningful (Jarrett, 2021).

After clarifying the audience, selecting appropriate question types becomes essential. Likert-scale questions are effective for measuring satisfaction or agreement. Multiple-choice questions help identify patterns quickly. Ranking questions can reveal priorities among features or concerns. Open-ended questions provide deeper insight into user reasoning and emotions (Farrell, 2016). A balanced mix prevents fatigue while still capturing meaningful detail.

Writing the questions requires careful attention to clarity and neutrality. Questions should avoid leading language that pushes respondents toward a particular answer. They should avoid combining multiple ideas into one question. For example, asking whether an app was “fast and easy” forces users to evaluate two qualities simultaneously. Each question should address one concept at a time, using simple and accessible language.

Survey length significantly impacts completion rates. Most UX surveys should take no more than five minutes to complete. Keeping the survey between five and fifteen questions helps maintain engagement. If more data is needed, it is often better to conduct multiple short surveys over time rather than one lengthy questionnaire (Krosnick, 1991).

Before distributing the survey widely, piloting it with a small group is crucial. A pilot test helps identify confusing wording, technical errors, and unnecessary questions. Timing the pilot participants also provides insight into whether the survey feels manageable.

Distribution strategy plays a major role in participation. Surveys can be shared through email, in-app prompts, QR codes, or campus networks. Clear communication about the purpose of the survey and how the results will be used encourages trust and participation. Even small incentives can increase response rates.

After collecting responses, the analysis should focus on patterns rather than isolated comments. Quantitative responses can be averaged to identify trends, while qualitative responses should be grouped into recurring themes. The ultimate goal is to translate findings into actionable recommendations. Without an action plan, the effort invested in creating and distributing the survey loses its value.

For beginners, the most important advice is to start small. Focus on one research question, write concise questions, and respect the time of participants. The strength of a UX survey lies not in its complexity but in its clarity.

Tools and Resources

Free and low-cost tools available to students include Google Forms (free with any Google account; unlimited questions and responses, basic branching logic), Microsoft Forms (included with Augusta University accounts; integrates with campus authentication for student-only surveys), and LimeSurvey (open-source, with a free tier suited to anonymous research). Whichever tool is used, build a reusable question bank: a consent statement for the first screen, a screener question, three to five Likert items, and one open-ended closer. For further reading, see Farrell (2016) on open-ended versus closed-ended questions, Sauro and Lewis (2016) for the statistics behind satisfaction measures, and this resource’s Research Ethics chapter for consent language appropriate to survey research.

Common Mistakes to Avoid

One common mistake is creating surveys that are too long. Excessive questions lead to fatigue, rushed answers, and low completion rates. Keeping surveys concise improves both response quality and participation.

Another frequent issue is vague question wording. Broad questions often generate unhelpful responses that cannot guide action. Precision and context are essential.

Leading questions also distort data. When wording implies a desired answer, respondents may unintentionally conform. Neutral phrasing preserves validity.

Some beginners overlook qualitative responses because analyzing open-ended feedback takes time. However, these responses often explain the reasoning behind numerical trends and should not be ignored.

Finally, collecting data without acting on it undermines trust. Surveys create an expectation that feedback will lead to change. Even small improvements based on findings demonstrate respect for participants and strengthen future research efforts.

Creating UX surveys requires discipline, clarity, and intentionality. When thoughtfully designed and carefully analyzed, they become powerful tools for understanding users and driving meaningful improvement.

Questions to Consider

1. The chapter argues that a focused research objective, such as understanding why users abandon a checkout process, produces a better survey than a vague one like "collect feedback." Take the vague objective and rewrite it as a focused research question. Then identify which question types from the chapter you would use to address it, and explain why each type fits.
2. A student shares this survey question with you before pilot testing: "Do you think the new campus portal is fast and easy to use?" Identify every problem with this question, name the principle from the chapter each problem violates, and rewrite it so that it is neutral, clear, and addresses one concept at a time.
3. Your team surveys students about the campus food delivery app and receives the following results: an average satisfaction rating of 2.1 out of 5, and this open-ended comment: "I never know if my order actually went through." Which finding tells you what the problem is, and which tells you how widespread it is? What decision would each finding inform, and what would you need before you could act on either one?
4. The chapter states that "collecting data without acting on it undermines trust." You run a survey, surface three significant pain points, present the findings to your project team, and six months later nothing has changed. What obligations does a researcher have to the participants who gave their time? Is there anything you could have done differently in the research plan stage to make action more likely?

References

Farrell, S. (2016, May 22). Open-ended vs. closed-ended questions in user research. Nielsen Norman Group. https://www.nngroup.com/articles/open-ended-questions/

Jarrett, C. (2021). Surveys That Work: A Practical Guide for Designing and Running Better Surveys. Rosenfeld Media.

Krosnick, J. A. (1991). Response strategies for coping with the cognitive demands of attitude measures in surveys. Applied Cognitive Psychology, 5(3), 213–236. https://doi.org/10.1002/acp.2350050305

Sauro, J., & Lewis, J. R. (2016). Quantifying the user experience: Practical statistics for user research (2nd ed.). Morgan Kaufmann.

Relevant Readings

Dillman, D. A., Smyth, J. D., & Christian, L. M. (2014). Internet, phone, mail, and mixed-mode surveys: The tailored design method (4th ed.). Wiley. The standard academic reference for survey methodology; provides rigorous grounding for the design choices this chapter covers at the practitioner level.

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner's guide to user research (2nd ed.). Morgan Kaufmann. Covers surveys alongside observation, interviews, and usability testing, making it useful for understanding where surveys fit in a larger research practice rather than treating them in isolation.

Sharon, T. (2016). Validating product ideas: Through lean user research. Rosenfeld Media. Organized around the specific questions product teams need to answer, with lightweight survey approaches matched to each; a practical companion for students who want to move from a research question directly to a method.

Usability Testing Basics

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter introduces usability testing, the evaluative method at the heart of user-centered design: watching representative users attempt realistic tasks with a product in order to discover where the design fails them. Readers will learn to plan a small moderated test, write tasks that do not lead, facilitate think-aloud sessions, and turn observations into prioritized, evidence-based recommendations.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Plan a small moderated usability test, including participant criteria, realistic task scenarios, and success measures appropriate to a defined research question.
2. Facilitate a test session using the think-aloud protocol and neutral prompting, without rescuing participants or steering their behavior.
3. Analyze session data to identify usability problems, rate their severity, and report findings as actionable design recommendations.

Main Chapter Text

What Usability Testing Is and Is Not

Usability testing is the practice of observing representative users as they attempt realistic tasks with a product, in order to identify where the design impedes them (Dumas & Redish, 1999). The definition’s every word is doing work. Representative users and not teammates, who know too much. Realistic tasks and not feature tours, which test nothing. Observing and not asking because this is a behavioral method, so it captures what people do, not what they predict or recall. And identifying problems and not validating the design, which is the mindset that quietly corrupts more tests than any procedural error. A usability test is also not a focus group, not a demo, and not an opinion survey; it is closer to a structured field observation in which the researcher controls the tasks but not the behavior.

The Logic of Small Samples

Usability testing earns its keep with remarkably few participants. Nielsen’s (2000) problem-discovery analyses showed that around five users per distinct user group typically surface the large majority of the problems a given round of testing can find, with later participants mostly re-encountering what earlier ones already revealed. The implication, developed further in this resource’s chapter on quick and dirty research, is to test in small iterative rounds, or find problems, fix them, and test again, rather than in one large expensive pass. The same logic sets the method’s limit: five participants can show that a problem exists and what it looks like, but never how often it occurs in a population. A usability test produces problems and not percentages.

Designing Tasks That Do Not Lead

Task wording is where tests are most often quietly invalidated. Consider a team testing a university library website. The task “Use the Course Reserves link in the Services menu to find your textbook” contains its own answer; the participant performs the instructions, and the team learns nothing. The honest version supplies a goal and a context, never a route: “Your professor said a copy of the course textbook is available through the library for free. Find out how you would get it.” Good tasks share that shape, scenario-based and stated in the user’s vocabulary rather than the interface’s labels with a defined end state the team can recognize as success or failure. Pilot the tasks on one person before the real sessions; the pilot almost always exposes a task that accidentally teaches, ambiguously ends, or cannot be completed at all (Rubin & Chisnell, 2008).

Moderation and the Think-Aloud Protocol

In a moderated usability test, a facilitator is present (in person or remotely) during the session. The moderator’s job is simultaneously straightforward and demanding. They must keep the participant talking, stay out of their way, and resist the urge to help. When a participant falls silent and stares at the screen, the moderator’s instinct is often to intervene. Doing so destroys the data. A stuck user is not a problem to be solved in the moment; it is evidence of a design failure to be documented.

The think-aloud protocol, developed by cognitive psychologists and adapted for usability research by Ericsson and Simon (1993) and later by Nielsen and others, asks participants to verbalize whatever they are thinking as they work through a task. Participants should detail their goals, their expectations, their confusions, and their reactions when something behaves unexpectedly. Think-aloud data is imperfect because people narrate some of their reasoning but not all of it, and the act of narrating can occasionally alter behavior. However, it provides a layer of interpretive context that silent observation alone cannot supply. When a participant says, “I’m clicking this because I think it means Settings, but I’m not sure,” the researcher learns not just that they clicked a particular element but why and that the labeling failed to communicate clearly.

Moderators should internalize a short list of neutral prompts: “What are you thinking right now?” “What would you expect to happen next?” “What would you do if this were your own computer?” These prompts encourage verbalization without steering the participant toward any particular interpretation or action.

Analysis, Severity, and Reporting

After a round of sessions, the researcher reviews notes, screen recordings, and think-aloud transcripts to identify usability problems. That is, instances where a participant struggled, failed, made an error, or expressed confusion. Problems are typically categorized by frequency (how many participants encountered it) and severity (how much it disrupts task completion). A five-point severity scale (0-4), popularized by Nielsen (1994), rates issues from cosmetic problems that need not be fixed immediately to catastrophic failures that prevent task completion entirely.

The output of a usability test is not a list of complaints; it is a set of design recommendations grounded in observed behavior. Each finding should link the observed evidence to a specific, scoped change, ordered by severity, or the reporting discipline developed in full in this resource’s chapter on writing research reports. In the library-website example, “four of five participants searched the catalog for the textbook and never opened the Services menu” becomes a recommendation about where course reserves must surface, not a complaint that users “missed” the link. The users missed nothing; the design did.

Questions to Consider

1. Explain, in your own words, why a usability test of five participants cannot support the claim “80% of users fail this task” — and what claim it can support instead.
2. Rewrite this leading task so that it tests rather than teaches: “Click the Account icon, then use the Order History page to find your last receipt.”
3. A participant goes silent for twenty seconds, visibly stuck. Script exactly what you would say — and what you must not say — and explain the difference in terms of the data.
4. Why does the think-aloud protocol justify its known imperfections? What would be lost in a fully silent test?
5. Think of a website task that recently frustrated you. Write it as a test scenario, define success, and predict the severity rating an evaluator would assign to the problem you hit.

References

Dumas, J. S., & Redish, J. C. (1999). A practical guide to usability testing (Rev. ed.). Intellect Books.

Ericsson, K. A., & Simon, H. A. (1993). Protocol analysis: Verbal reports as data (Rev. ed.). MIT Press.

Nielsen, J. (1994). Severity ratings for usability problems. Nielsen Norman Group. https://www.nngroup.com/articles/how-to-rate-the-severity-of-usability-problems/

Nielsen, J. (2000). Why you only need to test with 5 users. Nielsen Norman Group. https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/

Rubin, J., & Chisnell, D. (2008). Handbook of usability testing: How to plan, design, and conduct effective tests (2nd ed.). Wiley.

Relevant Readings

Barnum, C. M. (2020). Usability testing essentials: Ready, set … test! (2nd ed.). Morgan Kaufmann. A full-length, practice-oriented treatment of everything this chapter compresses, from recruiting through reporting.

Krug, S. (2014). Don’t make me think, revisited: A common sense approach to web usability (3rd ed.). New Riders. The mindset behind the method, in the most readable form available.

Portigal, S. (2013). Interviewing users: How to uncover compelling insights. Rosenfeld Media. Extends the chapter’s coverage of behavioral data collection by examining how post-observation interviews can deepen and contextualize usability findings.

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner’s guide to user research (2nd ed.). Morgan Kaufmann. Situates usability testing within a broader ecosystem of user research methods; particularly useful for understanding when testing is the right tool and when other methods should be prioritized.

Creating User Personas

Main Chapter Contributor: Aryn Broadnax

Supplemental Chapter Contributors: Trent M. Kays, PhD

Chapter Description

When it comes to user experience (UX) design, the foundational step toward creating meaningful and usable products is understanding your audience. This chapter explores the concept of user personas, which are semi-fictional, archetypical representations of a product’s core user base. Mastering the creation and application of user personas is essential for prioritizing the user’s needs throughout the entire product development lifecycle.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Define what a user persona is and explain its main purpose in the UX design process
2. Identify the components that make up an actionable user persona
3. Outline the steps required to gather research and synthesize data into a realistic user persona

Main Chapter Text

What Is a Persona?

Designing for yourself is one of the easiest traps for a product designer to fall into. When designers base important choices on their own preferences and tastes, they import their own biases into the design, and designers are not their end users. Their fluency with the product, their tolerance for complexity, and their tastes rarely match those of the people who will actually use it. To counter this bias and keep design user-centered, designers create personas (Kaplan, n.d.).

These personas are not cartoonish characterizations nor shallow stereotypes; they are highly detailed representations of the target users a product is aimed toward, developed from the ground up through rigorous, well-researched work. A persona is an archetype rooted in empirical data. Every part of a persona has been synthesized through extensive user research. The synthesis is transformative: it takes what tends to be huge and overwhelming amounts of abstract demographic and behavioral data and condenses it into a single, relatable character. By writing down a specific human being, the whole design team is able to stop thinking about abstract statistics and start thinking about users’ real and tangible problems (Dykes, 2025).

A good practice, then, is to check every major design decision against a persona. Doing so encourages empathy with the intended audience and keeps the design following the user’s needs rather than the designer’s taste. Products designed this way are not merely good-looking and functional — they feel useful to the people who will actually use them, which improves their odds in the marketplace and produces a richer, more integrated design process altogether.

What Makes an Effective Persona?

A well-crafted user persona is much more than a profile; it is a strategic, living document that communicates the full reality of the user’s context. Typically, a persona is brought to life with a fictional name, a quote that captures their mindset, and a photograph to make them feel tangible to the team (Kaplan, n.d.). The document must first establish the user’s demographics—like age, occupation, education level, and location—to set the stage for their daily life. But the real magic happens in the psychographics. An effective persona outlines the user’s specific behaviors, underlying motivations, ultimate goals, and daily frustrations.

For example, imagine you are developing a mobile application to help users organize, buy, and trade collectible photocards, or perhaps a digital platform designed to track progress through massive, multi-volume book series and translated dramas. The persona can’t just be “a 20-something student.” It needs to reflect the hyper-specific pain points of their reality: the anxiety of missing a limited-edition merchandise drop, the physical struggle of protecting and managing huge physical collections in binders, or the frustration of losing your place in a 100-chapter reading list. The more granular and relevant these details are, the more effective the persona becomes.

The Creation Process

Creating a user persona is a rigorous, multi-step process that actually begins long before any writing or interface sketching takes place, generally following a research-synthesis-drafting arc (Maze, n.d.). The first and most crucial phase is qualitative and quantitative user research. Designers must get out into the field to speak with actual users, deploy targeted surveys, and analyze existing market data to deeply understand the needs and habits of their target audience. Without this foundational research, a persona is essentially just a guess and guessing is a surefire way to lead product development astray.

Once the raw data is collected, the next step is synthesis. This phase involves sifting through interview transcripts and survey results to identify recurring themes, behavioral patterns, and overlapping goals among the participants. These verified patterns are grouped together to form the skeletal structure of the persona. After the data is segmented, the actual drafting begins. This is where a bit of narrative craft comes in. The persona is brought to life with a story that explains not just what the user does on a daily basis, but why they do it. It connects their emotional drivers to their technical behaviors.

Once finalized, the persona doesn’t just sit in a folder; it becomes an active, living tool. Design teams refer back to these personas during every single stage of development. Whenever there’s a disagreement, the persona acts as the tie-breaker. Teams will ask, “Would this specific feature actually solve our persona’s problem?” or “Does this interface layout make sense for their technical skill level and busy schedule?” By keeping the persona at the absolute forefront of the design process, teams ensure that the final product remains genuinely useful, empathetic, and user-centric.

Video resource: How to Choose the Scope of Your Personas [YouTube, 5:24 minutes, captions available]

Questions to Consider

1. How does a user persona differ from a simple demographic profile?
2. What are the risks of creating a user persona based on assumptions or stereotypes rather than concrete user research?
3. In what ways can a completed user persona be utilized during testing and evaluation?

References

Dykes, T. (2025, October 3). Personas make users memorable. Nielsen Norman Group.

https://www.nngroup.com/articles/persona/

Kaplan, K. (n.d.). Personas: Study guide. Nielsen Norman Group.

https://www.nngroup.com/articles/personas-study-guide/

Maze. (n.d.). A guide to user personas in UX. Maze. https://maze.co/guides/user-personas/

Relevant Readings

Cooper, A. (1999). The inmates are running the asylum: Why high-tech products drive us crazy and how to restore the sanity. Sams. Cooper introduced the persona as a design tool in this book, and reading it grounds students in both the intellectual origin of the method and the goal-directed design philosophy behind it.

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner's guide to user research (2nd ed.). Morgan Kaufmann. Situates persona creation within the broader toolkit of UX research methods, showing how the field research, interviews, and observation covered elsewhere in this book feed directly into the synthesis phase this chapter describes.

Pruitt, J., & Adlin, T. (2006). The persona lifecycle: Keeping people in mind throughout product design. Morgan Kaufmann. The most comprehensive book-length treatment of persona creation, validation, and organizational adoption available.

Discovery and the User’s Journey

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter introduces the discovery phase of user research—the early, open-ended work of understanding a problem before committing to a solution—and journey mapping as a method for capturing how people experience a process across time and touchpoints. Readers will learn to conduct basic discovery activities and to build journey maps that expose the gaps between how a service was designed and how it is actually lived.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Explain the purpose of the discovery phase and distinguish discovery research from evaluative methods such as usability testing.
2. Conduct basic discovery activities — stakeholder conversations, exploratory interviews, and review of existing evidence — in order to frame a researchable problem.
3. Construct an evidence-based user journey map that documents stages, actions, thoughts, and emotions across an experience and identifies pain points and opportunities.

Main Chapter Text

Before You Solve Anything

Most failed designs do not fail because they were built badly. They fail because they were built to solve the wrong problem. Discovery is the phase of research that exists to prevent this: the deliberately open-ended work done before a team commits to building anything, when the goal is not to evaluate a solution but to understand a situation. Norman (2013) describes good designers as people who resist solving the problem as it is handed to them, treating the stated problem as a symptom and searching for the underlying one. Discovery operationalizes that resistance. Where the methods covered elsewhere in this book (usability testing, heuristic evaluation) ask “does this design work?”, discovery asks the prior questions: What is actually going on here? For whom? And is the problem we were asked to solve the problem that matters?

What Discovery Looks Like in Practice

Discovery is not a single method but a posture applied through several activities. Teams typically begin with stakeholder conversations and talking with the people who commissioned the work to surface assumptions, constraints, and definitions of success that often turn out to conflict with one another. They review existing evidence: support tickets, analytics, prior studies, search logs, which are the traces users have already left. And they conduct exploratory interviews and observation with users themselves, asking about experiences rather than reactions to a design that does not yet exist. Portigal (2013) notes that early-stage interviewing demands a particular discipline: the researcher must remain in the problem space, resisting the gravitational pull of participants (and teammates) who want to jump straight to proposed features. A useful discovery output is not a list of feature requests; it is a defensible account of what people are trying to accomplish, where they struggle, and why.

The User’s Journey: Experience Across Time

Many of the most consequential findings in discovery are invisible in any single interaction, because they live in the seams between interactions. A student applying for financial aid does not experience “the portal”; she experiences a months-long sequence beginning with an award letter, a confusing email, a form that requires a parent’s signature, and a hold she discovers only at registration that spreads across systems owned by offices that never talk to each other. The journey, not the screen, is the unit of experience. Journey mapping is the discovery method built for this reality: a chronological account of how a person moves through a sequence of stages toward a goal, layered with what they do, think, and feel along the way (Kalbach, 2016; Gibbons, 2018). Service design extends the same logic to the organization’s side of the curtain, mapping the staff actions and backstage systems that produce each user-facing moment (Stickdorn et al., 2018).

Anatomy of a Journey Map

A journey map is typically structured as a timeline of stages. For the financial aid example, perhaps Awareness, Application, Verification, Award, and Disbursement with horizontal lanes recording the user’s actions, their thoughts and questions, their emotional state, and the touchpoints involved at each stage. Beneath the lanes, teams mark pain points and opportunities. Two disciplines separate useful maps from decorative ones. First, every cell must be grounded in evidence from real research (interviews, observation, support data) not invented in a conference room; a map of what the team imagines users feel is a record of the team’s assumptions, dressed up as findings (Kalbach, 2016). Second, the map must represent a specific user and goal, often anchored to a persona, because “everyone’s” journey is no one’s journey.

From Map to Opportunity

The analytical payoff of a journey map is the pattern it makes visible. Emotional low points cluster at predictable places: transitions between systems, waits with no status information, moments where the burden of coordination falls on the user. These are the moments that matter, and they are frequently moments no single team owns, which is precisely why no one has fixed them. In the financial aid case, the worst moment may not be any form but the silence between submission and decision; the highest-value design response may be a status notification, not a redesigned interface. Discovery work of this kind reframes what gets built. It is slower than jumping to solutions, and it is the single cheapest insurance a project can buy against building the wrong thing well.

Questions to Consider

1. In your own words, what distinguishes a discovery question from an evaluative question? Rewrite “Is our advising website easy to use?” as a discovery question.
2. Think of a multi-step process you have recently been through at your institution — registration, housing, financial aid, parking appeals. Sketch its stages from memory. Where were the seams between systems or offices, and what happened to you there?
3. Why does this chapter insist that every cell of a journey map be grounded in research evidence? What specifically goes wrong when a team maps an imagined journey?
4. A stakeholder hands your team a solution: “Students need a mobile app for the food pantry.” What discovery activities would you run before agreeing, and what might they reveal instead?

References

Gibbons, S. (2018). Journey mapping 101. Nielsen Norman Group. https://www.nngroup.com/articles/journey-mapping-101/.

Kalbach, J. (2016). Mapping experiences: A complete guide to creating value through journeys, blueprints, and diagrams. O’Reilly Media.

Norman, D. (2013). The design of everyday things (Rev. ed.). Basic Books.

Portigal, S. (2013). Interviewing users: How to uncover compelling insights. Rosenfeld Media.

Stickdorn, M., Hormess, M. E., Lawrence, A., & Schneider, J. (2018). This is service design doing: Applying service design thinking in the real world. O’Reilly Media.

Relevant Readings

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner’s guide to user research (2nd ed.). Morgan Kaufmann. Situates discovery activities within the full research lifecycle; chapters on field visits and diary studies extend this chapter’s methods.

Polaine, A., Løvlie, L., & Reason, B. (2013). Service design: From insight to implementation. Rosenfeld Media. Develops the service-design perspective on journeys, including the backstage processes that produce user-facing experiences.

Heuristic Evaluations for All

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter introduces heuristic evaluation, an inspection method in which evaluators judge an interface against established usability principles instead of testing it with users. Readers will learn the ten heuristics most widely used in the field, how to conduct and document an evaluation with severity ratings, and why inspection complements but never replaces watching real users.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Explain the purpose of heuristic evaluation and how inspection methods differ from user-based testing in cost, speed, and the kinds of problems they find.
2. Apply Nielsen’s ten usability heuristics to an interface, documenting each problem with its location, the heuristic violated, and a severity rating.
3. Evaluate the limits of heuristic evaluation and determine when its findings should be checked against usability testing or other user-based methods.

Main Chapter Text

Evaluation Without Users

Not every evaluation requires participants. Heuristic evaluation, introduced by Nielsen and Molich (1990), asks trained or even novice evaluators to examine an interface systematically against a short list of usability principles (heuristics) and to record every place the design violates one. The method was developed explicitly as “discount” usability engineering: a technique cheap enough, fast enough, and learnable enough that teams with no budget and no lab could still evaluate their designs. That is why this chapter’s title says for all. A student with a laptop, a checklist, and an afternoon can conduct a legitimate heuristic evaluation of the campus library catalog. The method asks for rigor, not resources.

The Ten Heuristics

The most widely used heuristic set is Nielsen’s ten, refined from the original work with Molich and still maintained as the field’s common reference (Nielsen, 1994; Nielsen, 2024). In compressed form: the system should keep users informed of its status; it should speak the users’ language rather than the system’s; users need clearly marked exits and undo; the design should follow platform conventions consistently; it should prevent errors rather than merely report them; it should favor recognition over recall, keeping options visible instead of forcing memorization; it should offer accelerators for experienced users while remaining usable by novices; it should omit irrelevant information; its error messages should state the problem in plain language and suggest a remedy; and help, where needed at all, should be searchable, concrete, and short. None of these is a law. Each is a rule of thumb that encodes decades of observed user struggle, which is what makes a violation worth recording even before any user confirms it.

Conducting an Evaluation

Procedure matters more than novices expect. Evaluators work independently first, comparing notes only afterward, because discussion too early collapses independent judgment into groupthink. Each evaluator passes through the interface at least twice: once to learn its flow, once to inspect element by element. Every problem is documented as a separate record: where it occurs, what happens, which heuristic it violates, and how severe it is. Nielsen and Molich's (1990) original experiments found that single evaluators discovered only 20–51% of an interface's usability problems, while aggregates of three to five independent evaluators performed substantially better. The practical basis for this would be a class exercise in which several students evaluate the same site and aggregate results. Severity is typically rated on a four-point scale, from cosmetic problems to catastrophes that block task completion, so that the resulting report ranks what to fix first rather than presenting an undifferentiated complaint list.

What Inspection Misses

Heuristic evaluation has known failure modes, and honest practitioners name them. Evaluators find what the heuristics direct their attention toward, which means problems outside the list (a confusing information architecture, content written at the wrong reading level for the actual audience) can pass uninspected. Evaluators also generate false positives: violations that are technically real but that actual users sail past without noticing. And because evaluators are not users, inspection reveals nothing about real goals, real contexts, or real workarounds, which is the territory of observation and testing covered elsewhere in this book. Consider a student team that evaluated their library’s catalog and logged fourteen violations; when they later ran a five-participant usability test, only five of the fourteen caused visible trouble, and the test surfaced two serious problems the inspection had missed entirely. Neither method was wrong. Each saw what the other could not, which is the standard argument for pairing them (Nielsen, 1994).

Reporting What You Find

An evaluation’s value is realized in its report. Group findings by severity, not by the order you found them; phrase each finding as a claim about the interface (“The checkout page gives no indication that the order was received”) rather than a citation of the heuristic’s name; and attach a concrete, scoped recommendation to every finding rated serious or worse. The conventions of research reporting developed in this book’s chapter on writing research reports apply here without modification because inspection findings are evidence-based claims, and they should be written like it.

Questions to Consider

1. Why does the method require evaluators to work independently before comparing notes? What specifically is lost if they evaluate together from the start?
2. Choose any heuristic from the list and find one violation of it on a website you use weekly. Document it as this chapter prescribes: location, behavior, heuristic, severity.
3. The library-catalog vignette describes false positives and violations users never noticed. Should those still be fixed? What considerations would you weigh?
4. Heuristic evaluation costs almost nothing, while usability testing costs participant time and coordination. Under what circumstances would inspection alone be defensible, and when would relying on it alone be negligent?

References

Nielsen, J. (1994). Heuristic evaluation. In J. Nielsen & R. L. Mack (Eds.), Usability inspection methods (pp. 25–62). John Wiley & Sons.

Nielsen, J. (1993). Usability engineering. Academic Press.

Nielsen, J. (2024). 10 usability heuristics for user interface design. Nielsen Norman Group. https://www.nngroup.com/articles/ten-usability-heuristics/

Nielsen, J., & Molich, R. (1990). Heuristic evaluation of user interfaces. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 249–256. https://doi.org/10.1145/97243.97281

Relevant Readings

Krug, S. (2014). Don’t make me think, revisited: A common sense approach to web usability (3rd ed.). New Riders. An accessible companion whose plain-language usability principles overlap heavily with the formal heuristics; useful for internalizing the mindset behind the checklist.

Wilson, C. (2014). User interface inspection methods: A user-centered design method. Morgan Kaufmann. Surveys the full family of inspection methods beyond heuristic evaluation, including cognitive walkthroughs and perspective-based inspection.

Barnum, C. M. (2020). Usability testing essentials: Ready, set … test! (2nd ed.). Morgan Kaufmann. The natural next step: how to verify inspection findings with real participants.

Writing Research Reports

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter treats the research report as a rhetorical document written for particular readers and to support particular decisions rather than as a neutral container for findings. Readers will learn to anchor claims in observed evidence, structure reports for busy decision-makers, and adapt one study’s results into multiple genres without distorting what the research can support.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Analyze the audience and purpose of a research report and select an appropriate structure, length, and level of detail for that audience.
2. Write findings that link every claim to observed evidence, preserve the distinction between description and interpretation, and end in actionable recommendations.
3. Adapt a single study into multiple genres—full report, executive summary, and presentation—while keeping each version faithful to the evidence.

Main Chapter Text

The Report Is Rhetoric

Research that no one acts on might as well not have happened, and whether anyone acts on yours depends substantially on a writing problem. A research report is not a transcript of what you did; it is an argument addressed to specific readers who have limited time and a decision to make. This is familiar ground for professional writers: every document succeeds or fails relative to its readers’ situation, knowledge, and purpose (Schriver, 1997). Redish (2012) frames the same point operationally, that is readers come to workplace documents to do something, not to admire them, and the implication for research reports is direct. Before drafting, answer three questions: Who will read this? What decision does it need to support? And what is the least this reader must absorb for the research to have its effect?

Anchoring Claims in Evidence

The discipline this book teaches for field notes, or keep description separate from interpretation, returns here as the structural principle of the findings section. Every finding should be a claim a skeptical reader can check: what was observed (“four of five participants scrolled past the deadline notice without pausing”), what it likely means (“the notice does not read as actionable information”), and what should be done about it. Participant quotations and short video clips are the strongest evidence a qualitative report can offer, but they carry obligations: they must be representative rather than cherry-picked, and they must honor the consent and anonymization commitments made during the study. Equally important is restraint about magnitude. A five-participant study supports claims about the existence and character of problems, not about their frequency in a population, so “most users” is not a sentence a small qualitative study can write (Dumas & Redish, 1999). Reports that overclaim do not merely err; they spend the researcher’s credibility, which is the only currency the next report will have.

Structure That Serves Busy Readers

Barnum (2020) observes that the most common consumer of a usability report reads only its first page, which means the first page must carry the argument. Lead with an executive summary stating what was studied, the three to five findings that matter most, and the recommended actions. Order findings by severity and consequence, never by the chronology of your sessions because your reader does not care what you discovered first. And write headings that assert, not headings that label: “Students cannot find the withdrawal deadline” does work that “Finding 3” never will, because a reader who skims only the headings still receives the argument. The full apparatus (method, participant profile, task list, limitations) belongs in the report, but it belongs after the findings or in an appendix, available to the reader who needs to evaluate rigor and invisible to the reader who needs to act by Friday.

One Study, Many Genres

A single study routinely produces several documents: a full report for the record, a one-page summary for decision-makers, a slide deck for the readout meeting, sometimes a highlight reel of participant clips. Treat these as genre translations of one set of findings, not as opportunities to shade the message for each audience. The summary may omit the limitations section’s detail, but it may not omit the limitation that changes how a finding should be read. A practical workflow is to write the full report first because it is the version that forces intellectual honesty for every claim must sit next to its evidence and, then, compress outward, checking each shorter genre against the full version for drift.

Recommendations That Can Be Acted On

Weak reports end in recommendations like “improve the navigation.” Strong ones end in recommendations a reader could assign on Monday: specific (“move the withdrawal deadline above the registration button on the dates page”), scoped (what it touches and what it does not), and prioritized against the severity ratings established in the findings. Where the right fix is genuinely unknown, say so and recommend the next study instead of inventing a solution. A report is allowed to conclude that the team now knows precisely what it does not know.

Questions to Consider

1. Take any finding from a study you have conducted or read and write it three ways: as pure description, as interpretation, and as a recommendation. What changes between the three sentences?
2. This chapter says overclaiming “spends the researcher’s credibility.” Find or invent an example of a claim a five-participant study cannot support, and rewrite it as a claim it can.
3. Write an asserting headline for a finding you might plausibly discover about your institution’s course-registration system. Then write the label-style heading it replaces. Show both to a classmate and ask what they learned from each.
4. You must compress a twelve-page report into one page for a dean. What survives, what moves to an appendix, and what may never be cut no matter the length?

References

Barnum, C. M. (2020). Usability testing essentials: Ready, set … test! (2nd ed.). Morgan Kaufmann.

Dumas, J. S., & Redish, J. C. (1999). A practical guide to usability testing (Rev. ed.). Intellect Books.

Redish, J. C. (2012). Letting go of the words: Writing web content that works (2nd ed.). Morgan Kaufmann.

Schriver, K. A. (1997). Dynamics in document design: Creating texts for readers. John Wiley & Sons.

Relevant Readings

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner’s guide to user research (2nd ed.). Morgan Kaufmann. The reporting chapters offer templates and examples across multiple research methods.

Nussbaumer Knaflic, C. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons. Practical guidance for the charts and visual evidence that quantitative findings require.

Quesenbery, W., & Brooks, K. (2010). Storytelling for user experience: Crafting stories for better design. Rosenfeld Media. Extends this chapter’s argument about findings-as-narrative into the spoken and presented forms covered later in this book.

Design Thinking and UX

Main Chapter Contributor: Aryn Broadnax

Supplemental Chapter Contributor: Trent M. Kays, PhD

Chapter Description

This chapter explores how Design Thinking is a human-centered framework UX designers use to solve complex problems. It involves five non-linear phases: Empathize (researching the user), Define (stating the problem), Ideate (brainstorming solutions), Prototype (building basic models), and Test (gathering feedback). By prioritizing real user needs and constant feedback over assumptions, this process ensures the final product is genuinely useful.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Explain the fundamental principles of Design Thinking
2. Describe the five distinct, cyclical phases of the Design Thinking process (Empathize, Define, Ideate, Prototype, Test)
3. Analyze the relationship between Design Thinking and User Experience

Main Chapter Text

The Art of Problem Solving

In the world of UX design, we rely heavily on frameworks. A framework is essentially a conceptual tool, or a set of guiding principles and best practices that provides structure to the process of creation. Frameworks make collaboration possible across teams and organizations, and they channel creative effort toward solving real problems for real people. Design Thinking is among the most important paradigms a digital product designer could learn. Design Thinking is an iterative, human-centered approach to developing products and services: ideas flow, develop, and change in response to the real needs, beliefs, and behaviors of real people. It is extremely easy to mistake “design” for the final artifact that it produces, like a sofa, a website, or an app on your smartphone. But that equation is faulty. Design is more than an artifact. Design is a method for solving a problem.

The Origins of Design Thinking

Design Thinking did not appear from nowhere; it has a traceable intellectual history. It originated with Nobel laureate Herbert A. Simon, who laid its groundwork in The Sciences of the Artificial, first published in 1969. Simon famously wrote that "everyone designs who devises courses of action aimed at changing existing situations into preferred ones" (Simon, 1996, p. 111), framing design as an activity invariably tied to creating a better world. This idea kept changing over the decades. In the 1980s, researchers Nigel Cross and Bryan Lawson began to identify a distinctly 'designerly' way to solve problems (Cross, 1982; Lawson, 2006). Cross proposed that design was not searching for a single theoretically perfect solution but one that was widely acceptable. At around the same time Peter Rowe, a longtime Harvard professor of architecture and urban design, wrote a book entitled Design Thinking that examined the situational logic and decision-making processes designers use (Rowe, 1987).

In the 1990s, the design agency IDEO helped redirect the trajectory of Design Thinking from an exclusively academic concept to a commercial powerhouse strategy (Brown, 2009). They promoted it as a novel approach to complex organizational problems and, in particular, to what planning scholars Horst Rittel and Melvin Webber (1973) had termed “wicked problems”: thorny, complex issues that simply don’t conform to established, black-and-white norms of right and wrong. Today the Hasso Plattner Institute of Design at Stanford (better known as the d.school) is seen as one of the most prominent institutions teaching this approach (Hasso Plattner Institute of Design at Stanford, n.d.), showing exactly why it has crossed the frontiers of conventional design to generate transformation across a wide array of industries.

Designing for the Real World

One of the key principles of Design Thinking is keeping the user at the core of the product development process and designing for actuality. You can illustrate this with a practical instance: the everyday vegetable peeler. Years ago, Sam Farber noticed the way his wife struggled with a conventional metal peeler with a thin metal handle because she had arthritis in her hands. Farber didn’t just want a peeler that looked better. He wanted to solve her pain. He looked to the user: to what hurt and how. After several revisions he produced a peeler with a larger handle of soft rubber that gave a stable and comfortable grip. This is the OXO peeler story. And the revision didn’t serve only his wife because the product was more comfortable and easier for everyone to use, it made a difference for people without arthritis as well, and a basic kitchen tool underwent a revolution. That’s Design Thinking in action.

The Five Phases of Design Thinking

The modern Design Thinking framework generally consists of five phases. Remember, however, that the process is non-linear: designers do not march from step one to step five in order. Designers are always hopping from one phase to another, constantly revising, tweaking, and updating as they learn new information.

Phase 1: Empathize

Everything begins with empathy, which is the north star throughout the process. You can’t design a solution without first understanding the user from the inside out. What do they love? What do they hate? What are they aiming for and what are their frustrations? What inspires them to take action? At this stage, designers must jettison their own assumptions and beliefs. Instead, they turn to research approaches such as user surveys, one-on-one interviews, and field studies where you observe users as they’re interacting with products in their natural settings. You could also study competitor products to understand how users currently describe and solve their daily challenges. This research can help you craft User Personas (research-based composite users) and User Experience Maps (visualizations of a user’s path through a whole process) so the team stays focused on real human requirements.

Phase 2: Define

The data gathered in the Empathize phase now frames what you can credibly say. In the Define phase you return to the problem and search for patterns. What did you hear most often? What specific roadblocks were users hitting? The most important result of this phase is a defined problem statement. This statement needs to be entirely user-centric. If business goals overshadow user needs here, the whole process will go awry. A good problem statement reads something like “Millennials in NYC need a way…” rather than “Our team will build an app to…” You may also draft a value proposition explaining why a user should choose what you create. The problem can be slippery, and you may need to revisit this definition as you learn more.

Phase 3: Ideate

This is where the user’s requirements and the problem you have identified converge, and it’s time to get creative. Ideation aims to produce as many ideas as it can, in a short time. You should never settle for your first idea, as the most obvious solution is rarely the right one. Ideation is a team effort that crosses functions: marketing, engineering, product management and so on. Brainstorm without judgement; there are no good or bad ideas at this stage, no matter how surreal things may seem. Teams use a variety of techniques to spark creativity:

• Worst Possible Idea: A great icebreaker where teams purposely come up with terrible solutions, which relieves pressure and often leads to alternative, brilliant ideas.
• SCAMPER: A checklist technique where you take an existing idea and Substitute, Combine, Adapt, Modify, Put to another use, Eliminate, or Reverse it to improve it.
• Freewriting: Setting a timer and writing out ideas uninterrupted, without self-editing.
• Mind Mapping and Word Banking: Using simple tools like pens and post-it notes to visually connect concepts.

Once you have a huge pool of ideas, you analyze them, home in on the most credible ones and build user flows to figure out how someone would really use the solution.

Phase 4: Prototype

Your shortlisted ideas get transformed into experimental models. Prototypes do not need to be polished and, in truth, keeping them rough is critical. You could begin building low-fidelity prototypes, which are rough paper sketches, storyboards, basic wireframes. There’s a very important trade-off in prototyping: the more time and money you spend to make the prototype look perfect, the slower the project moves and the more expensive it will be. A polished prototype can also backfire in testing: when a design looks finished, participants are more likely to feel intimidated and to hold back critical feedback. Prototyping enables designers to test the feasibility of solutions as soon as possible without incurring heavy development costs and feature creep. The aim remains that you always produce something that can be easily and quickly validated.

Phase 5: Test

In the Test phase, the prototype finally meets actual users. Facilitators lead participants through structured sessions, asking them to think aloud as they move through the prototype, in order to check the design decisions made in earlier phases. Testing doesn’t just happen at the very end, but it is deeply interconnected with the other phases. You should test the smallest things early, iterate based upon feedback from users, and review them again later, whether on a laptop, tablet, or smartphone. Throughout the entire process, testing forces everyone to find out whether the solutions generated work and actually improve the user’s experience. Problems are discovered before development teams take over. At this stage, feedback is what drives the iterative nature of Design Thinking.

The feedback loop is a cyclic model where teams build and learn quickly. They can collect user responses via anonymous questionnaires, open-ended interview questions, or by monitoring public reactions to new ideas. Teams tweak their products toward the best outcome by allowing for experimentation. One caution about prioritizing feedback: changes that improve the fundamental experience for many users should generally come before minor polish but do not confuse “edge cases” with users the design is allowed to ignore. People with disabilities, people on slow connections, and people in unusual contexts are often labeled outliers, yet designing for them frequently improves the product for everyone, as the OXO peeler story earlier in this chapter demonstrates. Triage feedback by impact, not by how typical the affected user seems.

People-First Partnership

In other words, design thinking and UX are a people-first partnership. Both are fundamentally user-oriented fields trying to empower people. UX designers are inherently problem solvers who are working to help users navigate technology to the best of their ability in order to get what they desire. And when design thinking methods are applied, UX designers improve the odds the end result—the software or product—is one that is user-friendly and pleasant.

Great UX that employs this user-driven (and solution-based) approach results in greater user satisfaction, more brand loyalty and ultimately, improved conversions and more revenue for the business. Design Thinking is not magic. It’s the systematic, empathetic, rigorous process that elevates a good idea to a truly great user experience.

Video resource: Design Thinking in UX [YouTube, 4:07 minutes, captions available]

Questions to Consider

1. Why is “empathy” considered the most critical starting point in the Design Thinking process?
2. How does the concept of a “Wicked Problem” relate to Design Thinking, and why is this framework particularly suited to address these types of issues?
3. In the prototyping phase, why is it often recommended to test low-fidelity models (such as paper sketches or basic wireframes) rather than waiting to test a highly polished, expensive software model?

References

Brown, T. (2009). Change by design: How design thinking transforms organizations and inspires innovation. HarperBusiness.

Cross, N. (1982). Designerly ways of knowing. Design Studies, 3(4), 221–227. https://doi.org/10.1016/0142-694X(82)90040-0

Hasso Plattner Institute of Design at Stanford. (n.d.). About the d.school. https://dschool.stanford.edu/about

Lawson, B. (2006). How designers think: The design process demystified (4th ed.). Architectural Press.

Rittel, H. W. J., & Webber, M. M. (1973). Dilemmas in a general theory of planning. Policy Sciences, 4(2), 155–169. https://doi.org/10.1007/BF01405730

Rowe, P. G. (1987). Design thinking. MIT Press.

Simon, H. A. (1996). The sciences of the artificial (3rd ed.). MIT Press.

Research Ethics

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter translates research ethics from principle into procedure, walking through the practical obligations, such as informed consent, recruitment, data management, and institutional review, that govern user research conducted by students and professionals. Where “The Ethics of User Thinking” asks why ethical research matters, this chapter shows what ethical research requires you to actually do.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Prepare an informed-consent process for a small user research study, including a plain-language consent script or form appropriate to the method being used.
2. Determine when a student research project requires Institutional Review Board (IRB) review and describe the steps for seeking guidance at their institution.
3. Apply responsible data-management practices, including anonymization, secure storage, and planned disposal, to notes, recordings, and survey responses.

Main Chapter Text

From Principles to Procedures

Ethical principles only protect people when they are translated into routine practice. The foundational principles of human-subjects research in the United States (respect for persons, beneficence, and justice) were articulated in the Belmont Report (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979) and implemented in federal regulation through the Common Rule (U.S. Department of Health and Human Services, 2018). For a student conducting a usability test in the library or a survey of classmates, those documents can feel remote. They are not. They answer the practical questions every study raises: Does this person understand what they are agreeing to? Could participation harm them? Who carries the risk, and who gets the benefit?

Informed Consent in Practice

Consent is a process, not a signature. Before any observation, interview, test, or survey begins, participants need a plain-language explanation of what the study involves, what data will be collected, how it will be stored and used, that participation is voluntary, and that they can stop at any time without penalty (Portigal, 2013). For an interview or usability session, this usually means a short verbal script plus a one-page form; for a survey, a consent statement on the first screen. Two details matter more than novices expect. First, recording requires its own explicit permission and never assume that consent to participate includes consent to be recorded. Second, consent language must match what you will actually do: if your form says responses are anonymous, you cannot collect email addresses for a follow-up raffle without revising it.

When the IRB Is and Is Not Involved

Institutional Review Boards review research involving human subjects before it begins. Whether a project requires review depends primarily on its purpose: classroom exercises conducted solely for learning generally do not require IRB review, while projects intended to produce generalizable knowledge, that is anything you plan to publish, present, or distribute beyond the course, generally do. The line is not always obvious, and the consequences of guessing wrong fall on participants as well as researchers. The working rule for students is simple: when in doubt, ask before collecting data. At Augusta University, the IRB and the course instructor are the first points of contact. Note that industry UX research typically operates outside IRB structures entirely, which removes the oversight but none of the obligation. This theme was developed in “The Ethics of User Thinking.”

Recruitment, Power, and Compensation

How you recruit shapes whether consent is genuinely voluntary. Asking friends, classmates, or people who report to you introduces social pressure that a consent form cannot remove; participants may agree because refusing feels awkward. Recruit broadly where possible, make refusal easy and consequence-free, and be especially careful with populations who have less power in the situation, such as students recruited by an instructor or employees recruited by a manager. Compensation should thank people for their time without becoming so large that it pressures participation. For most student projects, a small gift card or simple reciprocity is proportionate.

Data Management: Anonymize, Secure, Dispose

Every method in this book produces data about identifiable people, including field notes, recordings, transcripts, survey exports. Three habits keep that data safe. Anonymize early: replace names with participant codes (P1, P2) in notes and transcripts, and store the key linking codes to identities separately from the data. Secure storage: keep files on university-managed or encrypted storage, not on shared family computers or unsecured cloud folders. Planned disposal: decide before collection how long you will retain data and delete it on schedule, including recordings, which carry the highest re-identification risk. If you promised confidentiality in your consent process, these habits are how you keep that promise.

A Student Scenario

Consider a realistic case. A student team wants to test the campus parking app by observing five classmates using it, recording their screens, and surveying them afterward. The procedural checklist looks like this: confirm with the instructor whether the project stays inside the classroom (no IRB review) or will be presented publicly (ask the IRB); write a one-page consent form covering observation, screen recording, and the survey separately; recruit through a class announcement rather than personal pressure; assign participant codes before the first session; store recordings in a university OneDrive folder shared only with the team; and delete recordings at semester’s end. None of these steps is difficult. All of them are easy to skip, which is precisely why they belong in a checklist rather than a good intention.

Questions to Consider

1. Draft a three-sentence verbal consent script for a usability test of a course-registration website. What must it include, and what jargon must it avoid?
2. Your study is a class assignment, but your results are so interesting that you now want to present them at an undergraduate research conference. What changes, procedurally, and when should you have anticipated this?
3. A friend agrees to be interviewed but seems hesitant when you ask to record. What are your options, and what does each one cost you as a researcher?
4. Where, specifically, would you store interview recordings for a class project at your institution and where would you be tempted to store them instead? What is the actual difference in risk?

References

National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research. (1979). The Belmont report: Ethical principles and guidelines for the protection of human subjects of research. U.S. Department of Health, Education, and Welfare. https://www.hhs.gov/ohrp/regulations-and-policy/belmont-report/index.html

Portigal, S. (2013). Interviewing users: How to uncover compelling insights. Rosenfeld Media.

U.S. Department of Health and Human Services. (2018). Federal policy for the protection of human subjects (‘Common Rule’), 45 C.F.R. § 46. https://www.hhs.gov/ohrp/regulations-and-policy/regulations/45-cfr-46/index.html

Relevant Readings

Association for Computing Machinery. (2018). ACM code of ethics and professional conduct. https://www.acm.org/code-of-ethics. The professional code most relevant to computing and design practice; useful for seeing how the field self-regulates outside IRB structures.

Brignull, H. (2023). Deceptive patterns: Exposing the tricks tech companies use to control you. Testimonium Ltd. Connects research and design ethics to manipulative interface practice; pairs well with “The Ethics of User Thinking.”

Salganik, M. J. (2018). Bit by bit: Social research in the digital age. Princeton University Press. Chapter 6 is one of the clearest treatments of research ethics for digital-age methods, written for non-specialists and freely readable online.

Quick and Dirty Research

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter makes the case for discount research: small, fast, admittedly imperfect studies conducted with the time, money, and access actually available rather than the resources an ideal study would demand. Readers will learn the logic of small samples, how to run guerrilla sessions in everyday settings, and how to keep “quick” from sliding into “sloppy.”

Learning Outcomes

By the end of this chapter, students will be able to:

1. Explain the cost-benefit logic of discount usability methods, including why small samples identify a large share of usability problems.
2. Plan and conduct a guerrilla research session in an everyday setting using free or low-cost tools while honoring consent obligations.
3. Communicate findings from quick studies with appropriate caveats, distinguishing the claims small fast studies can support from those they cannot.

Main Chapter Text

The Perfect Study You Will Never Run

Every researcher can describe the ideal study: dozens of carefully screened participants, a dedicated lab, weeks of analysis. Almost no one ever runs it. The insight behind discount usability engineering, as articulated by Nielsen (1989) and developed throughout his subsequent work, is that the comparison that matters is not between a quick study and a perfect one, but between a quick study and no study at all. A small, fast, methodologically modest study that actually happens will improve a design more than an immaculate study that remains hypothetical. This is not a license for carelessness; it is a claim about where rigor should be spent. Discount methods strip cost from recruitment, equipment, and sample size while preserving the parts of method that protect validity: a defined question, scripted tasks, real notes, and honesty about limits.

Why Five Users

The most cited number in usability research is five. Nielsen’s (2000) analysis of problem-discovery data argued that the first five participants in a usability test typically reveal the large majority of the problems any single round of testing will find, with each additional participant mostly re-confirming what earlier ones already showed. The practical conclusion is not “five is enough research” but “run more rounds, not bigger ones”: three rounds of five, with fixes between rounds, will improve a design far more than one round of fifteen. The number carries real caveats. It applies per distinct user group and per task set (five students tell you little about faculty users) and it supports the discovery of problems, never quantitative claims about how often those problems occur. A five-person study that reports “80% of users failed” has confused a sample with a population; the honest sentence is “four of five participants failed, which suggests a problem worth fixing.”

Guerrilla Methods in Everyday Spaces

Guerrilla research takes the study to wherever users already are: a hallway intercept outside the dining hall, a fifteen-minute paper-prototype test at a library table, three questions asked of people waiting in line at the parking office. Krug (2010) domesticated this approach into a sustainable practice (one morning a month, three participants, the whole team watching) on the principle that frequent small tests beat rare large ones. The equipment list is deliberately short: the prototype or live site, a one-page task script, a notebook, and consent. That last item does not shrink with the study. A two-minute hallway test still requires telling people what you are doing, that participation is voluntary, and what you will do with what they say; recording of any kind still requires explicit permission. The procedures in this book’s Research Ethics chapter scale down gracefully, and “quick” has never been an exemption from them.

Keeping Quick From Becoming Sloppy

The failure mode of discount research is not smallness but shapelessness: a few hallway conversations, no defined question, impressions instead of notes, and a conclusion that was believed before the study began. Four habits prevent this. Write the research question down before recruiting anyone, because a study that cannot state what it is asking cannot fail to confirm it. Script the tasks, so every participant faces the same situation and differences between them are informative. Take real notes. The description-versus-interpretation discipline from the field observation chapter applies at full strength in a coffee shop. And debrief the same day, while the sessions are fresh, recording findings with the evidence that supports each one.

What Quick Methods Cannot Tell You

Honesty about limits is part of the method. Quick studies cannot produce statistics, cannot establish how widespread a problem is, and because convenience samples are made of whoever happens to be in the hallway, systematically miss users who are not there: people with disabilities, people off campus, people whose schedules or circumstances keep them out of the spaces where guerrilla research happens. A team that tests only with convenient users will build a product that works for convenient users. Quick and dirty research earns its keep as a fast feedback loop inside a practice that also includes deliberate recruitment, accessibility evaluation, and the slower methods this book covers and not as a replacement for them.

Questions to Consider

1. Explain the “rounds, not bigger ones” principle in your own words. Why do three rounds of five participants beat one round of fifteen?
2. Rewrite this claim from an imaginary hallway study so that it is honest: “80% of students can’t find the tutoring center’s hours on the website.”
3. Design a fifteen-minute guerrilla test you could run this week on a campus service: state the research question, three tasks, your location, and your consent script.
4. Who is systematically absent from the place you chose in the previous question? What would it take to hear from them, and which findings might change if you did?

References

Krug, S. (2010). Rocket surgery made easy: The do-it-yourself guide to finding and fixing usability problems. New Riders.

Nielsen, J. (1989). Usability engineering at a discount. In G. Salvendy & M. J. Smith (Eds.), Designing and using human-computer interfaces and knowledge based systems (pp. 394–401). Elsevier.

Nielsen, J. (2000). Why you only need to test with 5 users. Nielsen Norman Group. https://www.nngroup.com/articles/why-you-only-need-to-test-with-5-users/

Relevant Readings

Buley, L. (2013). The user experience team of one: A research and design survival guide. Rosenfeld Media. A field manual for doing credible research with no budget and no team; nearly every method in it is a discount method.

Nielsen, J. (1993). Usability engineering. Academic Press. The fuller statement of the discount philosophy, including the cost-benefit analyses behind it.

Sharon, T. (2016). Validating product ideas: Through lean user research. Rosenfeld Media. — Organized around questions teams actually ask, with lightweight methods matched to each; a natural companion to this chapter and the research planning chapter.

Creating a Research Plan

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter shows how to turn a vague curiosity into a researchable plan: a focused question, a method matched to that question, participants you can actually recruit, and a schedule that survives contact with reality. Readers will learn to write research questions, choose among the methods covered in this book, and document the plan in a single page that others can evaluate and reuse.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Write focused, answerable research questions and distinguish them from interview questions and from business or project goals.
2. Select research methods appropriate to a question’s position on the attitudinal–behavioral and qualitative–quantitative dimensions.
3. Produce a one-page research plan documenting background, questions, method, participants, schedule, and how findings will be used.

Main Chapter Text

Research Questions Are Not Interview Questions

Planning begins by separating three things that beginners routinely collapse. A project goal is what the organization wants (“increase use of the tutoring center”). A research question is what the study must learn for that goal to be pursued intelligently (“What do students believe the tutoring center is for, and what stops them from going?”). An interview question is what you actually say to a participant (“Tell me about the last time you got stuck in a course. What did you do?”). Notice that the research question never gets asked aloud, so participants cannot answer it directly and asking them to is a classic novice error. The research question disciplines everything downstream: a study that cannot state its question in one sentence cannot choose a method, cannot write a screener, and cannot know when it is finished (Goodman et al., 2012).

Matching Method to Question

With a question in hand, method selection becomes tractable. Rohrer's (2022) widely used framework locates methods along three dimensions; two are central to planning decisions at this stage. The first runs from attitudinal to behavioral (what people say versus what people do) and this book has already established why the two diverge: interviews and surveys capture beliefs and recollections, while observation and usability testing capture action. The second runs from qualitative to quantitative: small-sample methods that explain why and how, versus large-sample methods that measure how much and how many. The mapping is then mechanical in the best sense. A question about why students avoid the tutoring center is attitudinal and qualitative, or interviews. A question about whether they can complete the appointment form is behavioral and qualitative, or usability testing. A question about how many students know the center exists is attitudinal and quantitative, or a survey. Most projects need more than one cell of this grid and sequenced: qualitative discovery to find the right questions, then quantitative work to size them.

Participants, Recruiting, and Realism

A plan must name who counts as a participant precisely enough that a screener could be written from the sentence—“students” is not a population, but “first- and second-year students who have never used the tutoring center” is. It must also be honest about access. The recruiting that a professional team does with panels and incentives, a student team does with class announcements, flyers, and reciprocity, and the plan should reflect the recruitment you can actually execute rather than the one a textbook imagines. Sample sizes follow from method: a handful per user group for qualitative work, as the discount research chapter argues, and substantially more for anything that will be counted. Finally, the plan is where ethical commitments are made checkable, including consent process, recording permissions, data storage, and whether the project’s purpose requires IRB consultation, as detailed in the Research Ethics chapter.

The One-Page Plan

The deliverable this chapter asks you to master is deliberately short. A one-page research plan contains: background (what prompted the study, in two or three sentences); research questions (one to three, no more); method and rationale (which method, and why it fits the questions); participants (who, how many, how recruited, how consented); schedule (sessions, analysis, and reporting dates); and use (what decision the findings will inform, and who will receive them). The brevity is the point. A plan that fits on one page can be read by the stakeholders whose cooperation the study needs, and research that stakeholders have read and endorsed before it begins is research whose findings they are far more likely to act on afterward (Sharon, 2012). The plan is a persuasive genre as much as a procedural one, and it is how a researcher earns the right to be believed later.

Plans Change So Document Why

No plan survives the first week intact, and that is not failure. Pilots reveal that a survey’s respondents cannot articulate what only interviews will surface; a participant group proves unreachable; a question dissolves on contact with the field. Revise the plan when reality requires it, but revise it in writing, with a dated note recording what changed and why. The audit trail matters twice over: it keeps the team honest about whether changes were principled or merely convenient, and it turns every revision into documentation the next study can learn from.

Questions to Consider

1. Take the project goal “reduce the number of students who miss the course-withdrawal deadline” and write one research question and three interview questions for it. Keep the three categories distinct.
2. Place each of these questions on Rohrer’s two dimensions and name a fitting method: (a) Can transfer students complete the credit-evaluation form without help? (b) How many students know the form exists? (c) Why do students describe the transfer process as “scary”?
3. Write the participant definition for a study of your campus dining app, precisely enough that a screener could be built from it. Who does your definition exclude, and does that exclusion matter?
4. Why does this chapter call the research plan “a persuasive genre”? Who must be persuaded, of what, and what happens to findings when that persuasion never occurred?

References

Goodman, E., Kuniavsky, M., & Moed, A. (2012). Observing the user experience: A practitioner’s guide to user research (2nd ed.). Morgan Kaufmann.

Rohrer, C. (2022). When to use which user-experience research methods. Nielsen Norman Group. https://www.nngroup.com/articles/which-ux-research-methods/

Sharon, T. (2012). It’s our research: Getting stakeholder buy-in for user experience research projects. Morgan Kaufmann.

Relevant Readings

Farrell, S. (2017). UX research cheat sheet. Nielsen Norman Group. https://www.nngroup.com/articles/ux-research-cheat-sheet/. A compact map of which research activities fit which project stage; useful when drafting the method-and-rationale section of a plan.

Buley, L. (2013). The user experience team of one: A research and design survival guide. Rosenfeld Media. Includes planning templates scaled for researchers working alone or with minimal resources.

Portigal, S. (2013). Interviewing users: How to uncover compelling insights. Rosenfeld Media. The chapters on framing and preparation extend this chapter’s distinction between research questions and interview questions.

Presenting Your Research

Main Chapter Contributor: Trent M. Kays, PhD

Supplemental Chapter Contributors: Srinidhi Pothineedu, Aryn Broadnax, and Ana McGriff

Chapter Description

This chapter addresses the spoken and visual presentation of research findings, or the moment when weeks of work compete for twenty minutes of an audience’s attention. Readers will learn to structure findings as a narrative, design slides that carry evidence rather than duplicate speech, and respond to questions and skepticism by returning to what the study actually showed.

Learning Outcomes

By the end of this chapter, students will be able to:

1. Structure a research presentation as a narrative arc that moves an audience from problem to evidence to recommendation.
2. Design accessible presentation visuals—legible type, sufficient contrast, captioned media, and asserting headlines—that make evidence inspectable rather than decorative.
3. Respond to audience questions and pushback by distinguishing what the study supports, what it suggests, and what it cannot answer.

Main Chapter Text

Twenty Minutes for Six Weeks of Work

A research readout is an exercise in radical compression. The audience (a project team, a committee, or a dean) did not live through your sessions and will not read your appendices; they will give you a meeting slot and a fraction of their attention, and they need to leave the room knowing what you found and what they should do. The discipline, as in the written report, is to organize around the audience’s decision rather than your process. The most common structural mistake in student presentations is chronological method-first ordering, that is ten minutes of how the study was run before any finding appears, which spends the audience’s best attention on the part of the talk they need least. State the method in two sentences; if anyone wants more, that is what questions and appendix slides are for.

Findings as Story

Audiences retain narratives far better than lists, and research findings convert naturally into narrative form because every finding is already a small story: a person tried to do something, something happened, and it mattered (Quesenbery & Brooks, 2010). A readout built on this insight has an arc, such as here is the situation, here is what we watched people experience inside it, here is what that means for what we build next, rather than an inventory. Within the arc, let participants speak. A ten-second clip of a real student saying “I honestly thought I had already withdrawn” will move a room in a way no bullet point can, which is precisely why such clips carry obligations: they must be representative of the data rather than selected for drama, and they must respect the consent and anonymity commitments made when they were recorded. Storytelling in research presentation is a fidelity technique, not a persuasion trick; the moment the story outruns the evidence, it stops being research.

Slides That Carry Evidence

Slides fail in two familiar ways: as teleprompters the presenter reads aloud, and as decoration unrelated to the claims being made. Duarte (2008) offers the corrective principle: one message or idea per slide, with the visual doing work the spoken word cannot. For research readouts, that means the slide headline is the finding, stated as a full assertion (“Four of five participants missed the deadline notice”), and the body of the slide is the evidence: the quotation, the clip, the chart, the screenshot with the problem circled. When findings are quantitative, the chart must be honest before it is attractive. It must have axes that start at zero unless there is a stated reason and labels a reader can interpret without narration (Nussbaumer Knaflic, 2015). And a book that teaches accessibility owes its own presentations the same standard: type sized for the back of the room, color contrast that survives a projector, information never carried by color alone, and captions on every clip. Both are needed because audience members may need them and because conference rooms have unreliable audio.

The Room Pushes Back

Questions are not an interruption of the presentation; for skeptical audiences, they are the presentation. Anticipate the predictable challenges. “You only tested five people” deserves the discount-research answer this book has already developed: small samples reliably surface problems even though they cannot size them, and the finding is offered as a problem discovered, not a percentage measured. “That’s just one participant’s opinion” invites you to show the pattern across sessions, which you can do only if your notes were kept with the description-versus-interpretation discipline this book has insisted on since the observation chapter. And some questions have the only honest answer being “the study can’t tell us that.” Say it without apology. A presenter who visibly distinguishes what the evidence supports from what it merely suggests is borrowing nothing; a presenter who defends every claim equally is spending credibility that took the whole study to earn.

After the Applause

A readout that ends at the last slide changes nothing. Close by naming the decisions the findings inform and the specific recommendations on the table, distribute the one-page summary while attention is highest, and record what the room decided because six weeks later the presentation’s only remaining artifact will be what people remember agreeing to.

Questions to Consider

1. Convert this inventory item into a finding-as-story you could speak aloud in thirty seconds: “Task 3 success rate: 1/5. Issue: deadline notice placement.”
2. Write the asserting headline for a slide presenting that same finding, then sketch (in words) what evidence the slide body should contain.
3. A committee member says, “Five students isn’t data.” Draft your two-sentence reply.
4. Audit the last slide deck you made against this chapter’s accessibility standard: type size, contrast, color-only information, captions. What fails, and what would fixing it cost?

References

Duarte, N. (2008). Slide:ology: The art and science of creating great presentations. O’Reilly Media.

Nussbaumer Knaflic, C. (2015). Storytelling with data: A data visualization guide for business professionals. John Wiley & Sons.

Quesenbery, W., & Brooks, K. (2010). Storytelling for user experience: Crafting stories for better design. Rosenfeld Media.

Relevant Readings

Reynolds, G. (2012). Presentation Zen: Simple ideas on presentation design and delivery (2nd ed.). New Riders. A design philosophy for slides that complements Duarte; especially useful for presenters prone to text-heavy decks.

Tufte, E. R. (2006). The cognitive style of PowerPoint: Pitching out corrupts within (2nd ed.). Graphics Press. The classic polemic against slideware’s distortions; read it as a caution, then return to Duarte for the constructive program.

Barnum, C. M. (2020). Usability testing essentials: Ready, set … test! (2nd ed.). Morgan Kaufmann. The reporting chapters cover readout meetings and highlight reels alongside written deliverables.

Concepts and Resources

Defining Concepts

Main Chapter Contributors: Ana McGriff, Aryn Broadnax, and Srinidhi Pothineedu

Supplemental Chapter Contributors: Trent M. Kays, PhD

Affordable Learning Georgia (ALG)

Definition: Affordable Learning Georgia is a University System of Georgia initiative that funds faculty to create, adapt, and adopt free or low-cost course materials in place of expensive commercial textbooks. ALG-supported projects produce Open Educational Resources: openly licensed materials that anyone can access, use, and build upon at no cost.

Why It Matters: Textbook costs are a documented barrier to student success. Students who cannot afford required materials fall behind or drop courses entirely. ALG addresses this by funding the faculty labor it takes to create credible alternatives. The resources that result are not compromises; they are designed, reviewed, and revised by the same faculty who would otherwise assign a $150 commercial text.

In Practice: This very text is an ALG-funded OER. The course materials you are reading exist because faculty applied for and received support to build them rather than assigning a commercial textbook.

Related Concepts: Open Educational Resources (OER), Open Educational Practices (OEP)

Learn More: ALG Mission and Values https://www.affordablelearninggeorgia.org/about-us/missions-values/

Design Thinking

Definition: Design thinking is an iterative, human-centered approach to problem-solving that structures creative and analytical work into five stages: empathize, define, ideate, prototype, and test. Originally developed as a methodology in product and industrial design, it has spread into business, education, and research as a framework for tackling problems where the right question is not yet clear, let alone the right answer.

Why It Matters: Most hard problems in UX are not solved by applying the correct technique. They are solved by asking the right question in the first place. Design thinking provides a repeatable structure for doing exactly that: starting with deep understanding of the people affected by a problem before generating any solutions. Without that structure, research teams risk building precise answers to the wrong questions.

In Practice: A student team tasked with improving campus parking might assume the problem is a shortage of spaces. Design thinking would push them to empathize first: interview commuters, observe drop-off patterns, map the emotional experience of arriving late. Only then would they define the real problem, which might turn out to be signage, not supply.

Related Concepts: Discovery, Journey Mapping, User-Centered Design

Learn More: "What Is Design Thinking?" from the Interaction Design Foundation https://www.interaction-design.org/literature/topics/design-thinking

Ergonomics

Definition: Ergonomics, also called human factors, is the scientific discipline concerned with understanding how people interact with systems and environments, and with designing those systems to fit human capabilities, limitations, and needs. In UX, ergonomics extends well beyond the physical. Cognitive ergonomics addresses how design affects attention, memory, decision-making, and mental workload, making it directly relevant to every interface a researcher evaluates.

Why It Matters: A product that is ergonomically sound causes less friction, fewer errors, and less fatigue. Poor ergonomic design does not just frustrate users; it can make systems unusable for people with physical or cognitive differences, creating exclusion that good research should identify and good design should eliminate.

In Practice: A student designing a mobile app might apply ergonomic principles by placing frequently used controls in the thumb zone at the bottom of the screen, within reach without stretching, while ensuring that text contrast and tap target sizes meet standards that accommodate users with limited dexterity or low vision.

Related Concepts: Accessibility, Usability, Human-Computer Interaction (HCI)

Learn More: "What is Ergonomics?" from the Interaction Design Foundation https://www.interaction-design.org/literature/topics/ergonomics

Ethics

Definition: Research ethics in UX refers to the obligations researchers take on toward the people who participate in their studies: to be honest about what the research is and who will see it, to obtain genuine informed consent, to protect participant data, and to avoid causing harm through how findings are collected, used, or reported. These obligations derive from both professional codes and federal regulations that govern human subjects research.

Why It Matters: UX research puts real people, their behaviors, opinions, and sometimes their mistakes, in front of design teams and stakeholders. Researchers who treat consent as a formality, misrepresent how data will be used, or publish identifying information without permission are not just breaking rules; they are breaking the trust that makes future participation possible. Ethics in research is a precondition of research that means anything.

In Practice: A student running a usability test must explain the study's purpose to participants before recording begins, make clear that participation is voluntary and can stop at any time, and protect any recordings from being shared without explicit permission. A two-minute hallway session carries the same consent obligations as a formal lab study.

Related Concepts: Informed Consent, IRB Review, Research Ethics, Academic Integrity

Learn More: ACM Code of Ethics and Professional Conduct from the Association for Computing Machinery https://www.acm.org/code-of-ethics

Feng Shui (风水)

Definition: Feng Shui is an ancient Chinese practice whose name translates roughly as "wind and water." It focuses on arranging objects and spaces to create harmony between people and their environments. Rooted in Taoist philosophy, it holds that the layout of a space shapes the energy, well-being, and experience of the people who inhabit it.

Why It Matters: Feng Shui offers an early and culturally distinct example of design oriented around human experience rather than pure function. Its core insight, that spatial arrangement affects how people feel and behave, anticipates principles that contemporary UX research takes as foundational: environment shapes experience, and good design begins by understanding the person, not just the object.

In Practice: A student might draw on Feng Shui principles when thinking about a website's home page: deliberate use of white space to prevent visual clutter, clear pathways through the interface that guide rather than overwhelm, and a layout that produces calm and orientation rather than confusion. The vocabulary differs; the underlying concern with how design shapes experience does not.

Related Concepts: Ergonomics, User Experience (UX), Information Architecture

Learn More: "Feng Shui" from National Geographic Education https://education.nationalgeographic.org/resource/feng-shui/

Heuristic

Definition: A heuristic is a practical rule of thumb, a shortcut that produces a good-enough answer without exhaustive analysis. In everyday thinking, heuristics are the mental shortcuts people use to make fast decisions. In UX work, the term most often refers to usability heuristics: established principles of good interface design, such as "the system should always keep users informed about what is going on," that allow evaluators to identify design problems quickly without recruiting participants.

Why It Matters: Usability heuristics make evaluation fast and inexpensive. A trained evaluator can walk through an interface against a set of established principles in a fraction of the time and cost a full usability test requires, catching obvious problems early before those problems consume participant time. Understanding heuristics in both senses also helps researchers recognize when users' own mental shortcuts lead them astray in an interface, and design accordingly.

In Practice: Before running a full usability test on a campus dining app, a student might spend an hour walking through it against Nielsen's ten usability heuristics, flagging every screen that hides system status, lacks a clear path back, or uses error messages that blame rather than guide. Each fix those findings generate costs nothing to run and makes the subsequent usability test more productive.

Related Concepts: Heuristic Evaluation, Usability, Usability Testing

Learn More: “10 Usability Heuristics for User Interface Design” by Jakob Nielsen from the Nielsen Norman Group https://www.nngroup.com/articles/ten-usability-heuristics/

Human-Computer Interaction (HCI)

Definition: Human-Computer Interaction is the academic discipline concerned with how people interact with computing systems, and with designing those systems so the interaction is effective, efficient, and satisfying. HCI draws on cognitive psychology, computer science, and design, and it is the field from which most modern UX practice grew. The research methods, the vocabulary, and the ethical frameworks all have roots here.

Why It Matters: When a UX method has peer-reviewed evidence behind it, that evidence almost always lives in HCI literature. Knowing the field exists and what it covers tells you where to look when practitioner guidance is not enough: when a design decision is high-stakes enough to need something more rigorous than a blog post or a framework slide.

In Practice: A student evaluating why classmates abandon a campus registration portal mid-task is doing applied HCI: studying the interaction between a human and a computing system to locate where it breaks down. The methods they reach for, including think-aloud protocols, task analysis, and usability testing, were developed in HCI labs and refined through decades of published research.

Related Concepts: User Experience (UX), Usability, User-Centered Design, Ergonomics

Learn More: “Human-Computer Interaction (HCI)” from the Interaction Design Foundation https://www.interaction-design.org/literature/topics/human-computer-interaction

Multimodal

Definition: Multimodal refers to communication or learning that draws on multiple modes simultaneously: text, image, audio, motion, gesture, and spatial arrangement, rather than relying on any single channel. In educational contexts, multimodality describes the design of materials and environments that engage learners through more than one sensory or representational pathway. In UX contexts, it describes products and interfaces that communicate through several channels at once.

Why it Matters: No single mode reaches every learner or every user equally. Text-only instruction excludes people who process information better through audio or image; audio-only content excludes people who are deaf or in noisy environments. Multimodal design is both an equity strategy and an effectiveness strategy: materials designed across multiple modes are more accessible and more likely to be understood and retained by a wider range of people.

In Practice: A student creating an OER tutorial might combine written steps with annotated screenshots, a short narrated screen recording, and a downloadable summary checklist. Each element carries the same information through a different mode, so learners can engage through whichever combination works best for them.

Related Concepts: Accessibility, Universal Design for Learning, Multisensory Learning

Learn More: "The Power of Multimodal Learning" from Edutopia https://www.edutopia.org/visual-essay/the-power-of-multimodal-learning-in-5-charts/

Open Educational Resource (OER)

Definition: Open Educational Resources are teaching, learning, and research materials that reside in the public domain or have been released under an open license, meaning anyone may use, adapt, and redistribute them at no cost. OERs can take any form: textbooks, videos, lesson plans, assessments, or entire courses.

Why It Matters: Commercial textbooks can cost hundreds of dollars per course, and students who cannot afford them often go without. OERs eliminate that barrier by making materials available from the first day of class to every student with access to the internet. They also allow faculty to modify content to fit their students' actual context rather than working around a publisher's fixed structure.

In Practice: This textbook is an OER. You are reading it at no cost, and any instructor who wants to adopt it, adapt a chapter, or build on it for a different course is free to do so under the terms of its open license.

Related Concepts: Affordable Learning Georgia (ALG), Open Educational Practices (OEP), Repositories

Learn More: “Open Educational Resources” from UNESCO https://www.unesco.org/en/open-educational-resources

Persona

Definition: A persona is a fictional but evidence-based profile representing a key group of a product's users, built from patterns found in real user research rather than from assumptions. A persona typically includes a name, goals, behaviors, and frustrations, condensing research data into a single, relatable character that a design team can keep in view throughout a project.

Why It Matters: Design decisions made without a concrete sense of who will use a product tend to drift toward the preferences of whoever is in the room. Personas counteract that drift by anchoring decisions to actual users: when a team debates whether a feature is necessary, a well-built persona gives them a concrete way to ask and answer that question. Personas also create shared reference points across disciplines, so developers, writers, and designers are all working toward the same understanding of who they are building for.

In Practice: A student team redesigning a club's recruitment page might create "Jordan, a first-year commuter student who checks campus information between classes on a phone with inconsistent data service." Every layout decision can then be tested against whether it works for Jordan, not just for the student who happens to own a fast laptop.

Related Concepts: User-Centered Design, User Experience (UX), Empathy Mapping

Learn More: “Personas Make Users Memorable” from Nielsen Norman Group https://www.nngroup.com/articles/persona/

Scientific Management (Taylorism)

Definition: Scientific Management, also known as Taylorism, is a theory of management developed by Frederick Winslow Taylor in the early 1900s that applies systematic observation and measurement to work in order to find the most efficient way to complete a task. Taylor's method involved breaking complex jobs into small, discrete steps, timing each step with a stopwatch, eliminating wasted motion, and standardizing the result as the "one best way" to do the work.

Why It Matters: Taylorism introduced a core idea that UX research inherited and transformed: that you cannot improve work by guessing at it; you have to observe it. Task analysis in modern UX, the practice of watching exactly how a user completes a goal and mapping every step, is a direct descendant of Taylor's time-and-motion studies. Where Taylor used observation to maximize industrial efficiency, UX researchers use it to identify where a product's design is making users' work harder than it needs to be.

In Practice: A student redesigning a campus course-registration portal might use task analysis to document every click, decision point, and moment of confusion a student encounters when trying to add a class. That kind of granular observation, which Taylor would have recognized, surfaces friction that no amount of assumption-based design could predict.

Related Concepts: Task Analysis, Usability, Efficiency, Human-Computer Interaction (HCI)

Learn More: "Scientific Management" from Britannica https://www.britannica.com/topic/scientific-management

User

Definition: In UX research, a user is any person who interacts with a product, system, or service in order to accomplish a goal. Users are the subject of UX research: the people researchers observe, interview, and test to understand whether a design actually serves human needs.

Why It Matters: Everything in UX research begins and ends with users. A design that works for its intended users succeeds; a design that makes sense to its creators but confuses the people who use it fails, regardless of how polished it looks or how hard the team worked. Keeping real users visible in every design decision is the core commitment that separates user-centered practice from guesswork.

In Practice: When a student runs a usability test on a campus dining app, the people recruited to complete tasks are the users: the subject of the study, and what they do during those tasks is the data. The student team's own familiarity with the app is not a substitute for watching someone encounter it fresh.

Related Concepts: User-Centered Design, User Experience (UX), Persona, Usability Testing

Learn More: "The Basics of User Experience Design" from the Interaction Design Foundation https://www.interaction-design.org/literature/article/the-basics-of-user-experience-design

User Experience (UX)

Definition: User experience (abbreviated UX) refers to the overall quality of a person's interaction with a product, system, or service, and everything that shapes that interaction: whether they could accomplish their goal, how easy or confusing the process felt, and what emotional response the interaction produced. The term was popularized by cognitive scientist Donald Norman in the 1990s to capture something broader than visual design or technical function alone. UX is not what a product looks like; it is what using it feels like.

Why It Matters: A product can be technically functional and still fail its users. UX research exists to close the gap between how a designer imagines something works and how a real person actually experiences it. Every method in this book — interviews, observation, usability testing, surveys — is a way of learning something about user experience that no designer can learn by reasoning from their own perspective alone.

In Practice: A student testing the campus tutoring center's scheduling system is doing UX research: they are studying whether the experience of using that system matches the needs and expectations of the students it is supposed to serve and finding precisely where it does not.

Related Concepts: Usability, Human-Computer Interaction (HCI), User-Centered Design, Accessibility

Learn More: "User Experience (UX) Design" from the Interaction Design Foundation https://www.interaction-design.org/literature/topics/ux-design

User Centered Design

Definition: User-centered design, commonly abbreviated UCD, is an iterative design and development process in which the needs, goals, and contexts of real users are the primary input at every stage. Rather than treating users as the final audience for a finished product, UCD treats them as active participants in defining the problem, evaluating early solutions, and refining the result across successive cycles of research and revision.

Why It Matters: Most design failures are not technical failures; they are failures of assumption. Teams build what they imagine users need rather than what users actually need, and they discover the mismatch too late to fix it cheaply. UCD structures a process that surfaces that mismatch early, when changes are still inexpensive, by making real user feedback a routine part of development rather than an afterthought.

In Practice: A student team redesigning a campus health portal would practice UCD by interviewing students about their current experience before touching the interface, testing paper prototypes with real users before writing any code, and returning to users after each revision to check whether the changes actually helped. The cycle of research, revision, and return is the method; skipping any part of it is where UCD breaks down.

Related Concepts: Iterative Design, Contextual Inquiry, Usability Testing, User Experience (UX)

Learn More: "User-Centered Design (UCD)" from the Interaction Design Foundation https://www.interaction-design.org/literature/topics/user-centered-design

Helpful Resources and Links

Main Chapter Contributor: Ana McGriff

Supplemental Chapter Contributors: Trent M. Kays, PhD, Srinidhi Pothineedu, and Aryn Broadnax

How to Use This List

Resources are organized into two sections: Academic Resources (scholarly and professional articles directly supporting the textbook’s content) and Industry Resources (practitioner guides, reports, accessibility standards, and OER examples from across disciplines). Each entry follows APA 7 citation format, includes a 2–3 sentence annotation, and carries a resource type tag drawn from four categories: #Background (historical and contextual), #Research (data-driven studies and reports), #Learning (tutorials, guides, and textbooks), and #Commentary (analysis, opinion, and reflective writing).

Academic Resources

Dickerson, J. (2013, September 9). Walt Disney: The world’s first UX designer. UX Magazine. https://uxmag.com/articles/walt-disney-the-worlds-first-ux-designer

In this article, Dickerson argues that Walt Disney should be considered the first true UX designer due to his relentless focus on immersion and his practice of “plussing”—continuously improving the guest experience at Disney parks. This source is useful for illustrating how foundational UX concepts like user testing, feedback loops, and cross-functional collaboration existed long before the digital age.

Resource Type: #Background

Stevens, E. (2021, July 28). The fascinating history of UX design: A definitive timeline. CareerFoundry. https://careerfoundry.com/en/blog/ux-design/the-fascinating-history-of-ux-design-a-definitive-timeline/

In this article, Stevens outlines the evolution of User Experience design, tracing its origins from ancient practices like Feng Shui and Greek ergonomics to modern technological milestones set by companies like Apple and Xerox. It is particularly useful for establishing a chronological framework of the field, highlighting how diverse historical disciplines have contributed to contemporary UX principles. Note: CareerFoundry is ceasing operations—verify URL availability before assigning and consider an archived version as a backup.

Resource Type: #Background

Vinney, C. (2023, January 9). The history of UX (user experience). UX Design Institute. https://www.uxdesigninstitute.com/blog/history-of-ux/

In this short reading, Vinney outlines the history and background of contemporary user research and experience. She draws on the history of design and innovation from ancient origins to contemporary practices, highlighting the importance of understanding the trajectory and pathway of user experience and human interaction. This reading provides excellent background on the past, present, and potential futures of user experience, research, and design.

Resource Type: #Background

Industry Resources

Cyr, D. (2014). Emotion and website design. In M. Soegaard & R. F. Dam (Eds.), The encyclopedia of human-computer interaction (2nd ed.). Interaction Design Foundation. https://www.interaction-design.org/literature/book/the-encyclopedia-of-human-computer-interaction-2nd-ed/emotion-and-website-design

When it comes to creating a digital resource, thoughtfulness to the visual design and the reader is of utmost importance. Being mindful to not input too much or too little, although its original focus is on website design, I found the overall ideas touched on in Dianne Cyr’s “Emotion and Website Design” useful for putting effort into achieving the desired emotional response based on design elements and ease of access. If a source is too busy or difficult to navigate, the viewer will be less interested in continuing to explore. Students often already come into a digital resource perhaps stressed or anticipating boredom, and although our focus will be more geared to substantial academic content over aesthetics, such a notion should not be pushed aside. Although the source is over ten years old, I still found Cyr’s information insightful to our purpose.

Resource Type: #Research

Kalidindi, R. (2024, December 2). Neurodiversity: Inclusive user experience. UXPA Magazine. https://uxpamagazine.org/neurodiversity-inclusive-user-experience/

This source was more following a curiosity on my end, on what the author would suggest as helpful in catering to the neurodiverse in the context of UX. He presents an understanding and knowledgeable approach, wanting to present a “Human-centered approach” considering everyone involved, specifically those whose brains process things a great deal differently. Although a quite small resource compared to some of the others cataloged here, it lists a great deal of things to consider, such as sensory considerations, consistent layout and navigation, input flexibility, error handling, testing with diverse users, and most importantly keeping in mind user empathy.

Resource Type: #Learning

Loranger, H., Moran, K., & Nielsen, J. (2017). Designing for young adults (18–25) (3rd ed.). Nielsen Norman Group. https://www.nngroup.com/reports/designing-for-young-adults/

Loranger, Kate Moran, and Jakob Nielsen’s third edition of their report focuses on guidelines for making and designing resources for young adults, or college-aged people, classifying this bracket as being ages 18–25. Their focus is to dispel possibly harmful myths and stereotypes that can hinder proper presentation of digital resources to people within the focused age bracket. The report also addresses the difference in their needs and internet behavior compared to other generations, although the source is not immediately recent, the points they make, specifically on the discussion of younger generations being more prone to quick boredom and wanting their sites to be interactive and “fun,” are still relevant today.

Resource Type: #Research

Ronsen, M. (2025, July 24). Learning from AI-natives: What the next generation is teaching us about writing. User Interviews. https://www.userinterviews.com/blog/learning-from-ai-natives-next-generation-research-insights

I’ve made a point to evaluate sources that touch on our moreover younger student demographic. While it is easy to operate off of the assumption that those that are around us at AU now will benefit most from this project, and that assumption would be partially correct, it is the next classes and onward that will benefit most. To that notion, to acknowledge those people not yet college-aged is to acknowledge their prowess with AI and its heavy integration into academics, whether good or bad. Michele Ronsen’s blog post focuses on the difference between the younger generation using AI to solve a problem for them and using AI to help them solve their problems, and how from that, research tactics should pivot and adapt in the same way that the younger generations are doing so when it comes to their discovery and problem solving.

Resource Type: #Commentary

Steelman, L. (2025, August 29). Participant management: An in-depth guide for qualitative research. User Interviews. https://www.userinterviews.com/blog/participant-management-qualitative-research

Steelman’s blog post is not only the most recent of my findings, but also exceptionally well presented and explained. The blog breaks down the criteria that should be considered for qualitative participants to be selected, the kinds of questions to present to properly evaluate a project’s testing, and deep dives into the different methods of managing both the participants and their subsequent results, as well as considering methods for post-project follow-up. While Steelman ends the blog with a large portion of marketing promotion for User Interviews’ available programs, her clear knowledge and effort in explaining helpful processes for managing study participants proves useful.

Resource Type: #Learning

Stein, S. (2023, January 29). Color contrast: Infographics and UI accessibility. UXPA Magazine. https://uxpamagazine.org/color-contrast-infographics-and-ui-accessibility/

In this blog post, Stein goes over the importance of intentional design. Similarly to the source at the beginning of this document, Stein’s post, with the aid of visual examples, breaks down the viewer benefit of remembering the importance of UI design and information placement. The main focus of her blog is readability, or put more simply, the ability of the reader to clearly understand the information offered by given graphics, specifically in the matters of text color, text ratio, placement, and how such effort aids in maintaining accessibility for those who are visually impaired.

Resource Type: #Learning

Teo, Y. S. (2025, August 23). What is interaction design? Interaction Design Foundation. https://www.interaction-design.org/literature/article/what-is-interaction-design

In this blog post, Teo Yu Siang goes into what Interaction Design is. Apart from visual or aesthetic design, the goal of interaction design is to enable the user to achieve their objective in the best and most efficient way possible. The overlap between Interaction Design and UX design is clear and acknowledged by the author, and yet he further breaks down the explanation into five categories, even acknowledging how things like error code messages might present to the user and what inputs those kinds of messages might require or display, something I hadn’t even considered. While our overarching goal is to helpfully present academic information, we are still presenting a product, even if said product is intended to be an economically viable option.

Resource Type: #Background

U.S. Department of Justice. (2024, March 8). Fact sheet: New rule on the accessibility of web content and mobile apps provided by state and local governments. ADA.gov. https://www.ada.gov/resources/2024-03-08-web-rule/

In my limited research I was unable to find one single, authored source on governmental web design and accessibility. However, the official U.S. Web Design System or USWDS site did offer a tab on its acceptable design principles, such principles being those codified in the “21st Century Integrated Digital Experience Act,” as well as there being similar tabs in sites such as the ADA’s official website. For the purpose of our goal, I would focus on disability accessibility as outlined in some of the ADA official sites, ensuring the digital pages cooperate with screen readers, limiting moving components, and ensuring subtitle capabilities.

Resource Type: #Background

Open Educational Resources: Examples from Across Disciplines

The following entries illustrate the range of OER available across academic fields and serve as models of different approaches to open textbook design, format, and audience.

88 open essays: A reader for students of composition and rhetoric. (2021). Open Washington. https://openwa.pressbooks.pub/lwtech88readings/

This OER is a collection of 88 different essays from various authors covering a compilation of different topics like science, social issues, and politics. It’s perfect for getting ideas or just reading for a class. The resource is basically just a big list of the essays, so it’s mainly text-only.

Discipline: Rhetoric & Composition

Resource Type: #Learning

Introduction to engineering thermodynamics. (2022). BCcampus Open Education. https://pressbooks.bccampus.ca/thermo1/

This OER is a textbook on engineering thermodynamics covering all the core concepts, from the basics of thermodynamics to the different laws and properties of substances. It is highly interactive, featuring graded multiple-choice questions, clickable dictionary definitions for terms, and clickable diagrams.

Discipline: Engineering / Thermodynamics

Resource Type: #Learning

Scientific inquiry in social work. (2018). University of Minnesota Libraries Publishing. https://manifold.open.umn.edu/read/scientific-inquiry-in-social-work/

This OER is a detailed textbook that takes you through the entire process of research in social work, from the basics all the way to the advanced side like experimental design and different research methods. It has multimodal features like clickable links and a platform-specific ability to highlight text to create annotations.

Discipline: Social Work / Research Methods

Resource Type: #Learning

Speak out, call in: Public speaking as advocacy. (2019). University of Kansas. https://opentext.ku.edu/speakupcallin/

This OER is a take on a public speaking textbook that frames public speaking as a way to advocate for something you believe in. The book covers everything from figuring out your audience to making a compelling argument. It is multimodal, featuring clickable hyperlinks and embedded graphs and diagrams.

Discipline: Rhetoric & Public Speaking

Resource Type: #Learning

Technical writing essentials: Introduction to professional communications in the technical fields. (2019). BCcampus Open Education. https://pressbooks.bccampus.ca/technicalwriting/

This OER is a textbook for anyone who needs to get better at technical writing, like for a class or a job. It goes over all the basics, from how to organize documents to citing sources and giving presentations. It’s a straightforward, text-based resource.

Discipline: Technical Communication

Resource Type: #Learning

UNM core writing OER collection. (2023). University of New Mexico. https://nmoer.pressbooks.pub/unmcorewriting/

This OER is a writing textbook put together by the UNM Core Writing Program covering a ton of material, from critical thinking and reading to different writing genres like memoir and rhetorical analysis. It also gives tips on things like time management and emailing professors. It is a multimodal resource featuring clickable links, embedded videos, pictures, and diagrams.

Discipline: Rhetoric & Composition

Resource Type: #Learning

What does art come from? (2022). University of Texas at Arlington. https://uta.pressbooks.pub/wheredoesartcomefrom/

This OER is organized as a series of questions about art, covering 23 different topics related to art. As an art resource, its use of pictures, diagrams, and clickable links makes it a multimodal text.

Discipline: Art / Art History

Resource Type: #Learning