UI/UX Design User Research & Strategy Syllabus

What is contextual inquiry?

A field research method in which the researcher observes and interviews a user in their real environment (home, workplace) while they perform their actual tasks, asking questions in the flow of work. It reveals context, workarounds, and behaviors users would not recall or report in a lab or interview.

What are the four principles of contextual inquiry (Beyer and Holtzblatt)?

1) Context — study users in their real environment during real work. 2) Partnership — user and researcher collaborate, in a master–apprentice relationship where the user is the expert. 3) Interpretation — the researcher shares interpretations with the user to verify meaning. 4) Focus — a clear project scope steers what the researcher attends to.

Why do field studies often reveal findings that lab studies and interviews miss?

Because behavior is shaped by real context — interruptions, physical constraints, tools, collaborators, and workarounds. Users are poor at recalling or articulating habitual behavior in interviews (the say–do gap), so direct observation in situ captures what people actually do rather than what they say they do.

What is a diary study?

A longitudinal research method in which participants self-report their experiences, behaviors, and feelings over an extended period (days to weeks) in a diary — via app, messages, photos, or written logs — capturing behavior in context and over time without a researcher present.

When should you choose a diary study over a one-time interview?

When the behavior unfolds over time or is habitual, infrequent, or context-dependent — e.g., medication routines, commuting, multi-week purchase decisions — because recall of such behavior in a single interview is unreliable, while in-the-moment logging captures it accurately.

Contrast interval-contingent, signal-contingent, and event-contingent logging in diary studies.

Interval-contingent: participants log at fixed times (e.g., every evening). Signal-contingent: participants log whenever the researcher sends a prompt or notification. Event-contingent: participants log each time a defined event occurs (e.g., every time they order food online).

What is card sorting and what does it help design?

A method where participants group labeled cards (content or features) into categories that make sense to them, optionally naming the groups. It reveals users' mental models of content relationships and informs information architecture — navigation structure, menu grouping, and labeling.

Differentiate open, closed, and hybrid card sorting.

Open: participants create and name their own categories — best for discovering a new IA. Closed: participants sort cards into predefined categories — best for validating or fitting content into an existing structure. Hybrid: predefined categories exist but participants may add their own.

What is tree testing and how does it differ from card sorting?

Tree testing (reverse card sorting) gives participants a text-only version of a site's hierarchy and asks them to find where a specific item or task lives, measuring findability (success rate, directness, time). Card sorting generates a structure from users' mental models; tree testing evaluates a proposed structure — they are complementary.

What is affinity mapping (affinity diagramming)?

A collaborative analysis technique where individual observations or data points from research are written on separate notes, then grouped bottom-up by similarity into clusters, which are labeled with themes. It turns large amounts of messy qualitative data into organized patterns and insights.

Describe the basic process of building an affinity map from interview data.

1) Extract one observation, quote, or fact per sticky note from the raw data. 2) Place notes on a wall/board and group similar notes together, letting categories emerge bottom-up rather than using preset buckets. 3) Label each cluster with a descriptive theme. 4) Discuss clusters to derive insights and priorities.

What is thematic analysis in qualitative UX research?

A systematic method for analyzing qualitative data by coding it — tagging segments with short labels — and then organizing codes into recurring themes: patterns of meaning across the dataset that answer the research questions.

What are the six phases of thematic analysis (Braun and Clarke)?

1) Familiarize yourself with the data. 2) Generate initial codes. 3) Search for themes by grouping codes. 4) Review and refine themes against the data. 5) Define and name themes. 6) Produce the report.

What is the difference between inductive and deductive coding in thematic analysis?

Inductive (bottom-up): codes and themes emerge from the data itself without preconceived categories. Deductive (top-down): data is coded against a predefined framework or set of expected categories derived from theory or research questions. Studies often combine both.

In UX research, what distinguishes a finding (observation) from an insight?

A finding is a factual observation of what happened ("7 of 10 users scrolled past the filters"). An insight interprets why it happens and what it means — a non-obvious understanding of user motivation or behavior that suggests an opportunity ("users don't recognize the filters as interactive, so they brute-force scroll instead"). Insights are actionable; findings alone are not.

What is a user pain point, and what are the common types?

A pain point is a specific problem or source of friction users experience with a product or process. Common types: usability/interaction pain points (confusing UI, errors), process pain points (broken or tedious end-to-end journeys), financial pain points (cost, unexpected fees), and support pain points (can't get help when stuck).

How should research insights be prioritized after analysis?

By combining severity/impact on users (how much the problem blocks or frustrates them and how many users it affects, e.g., frequency $\times$ severity) with business value and feasibility of addressing it — focusing first on high-impact, widespread pain points aligned with product goals.

What is a research repository and why do teams use one?

A centralized, searchable system that stores research data, insights, reports, and artifacts — typically tagged and organized. It prevents insights from being lost, avoids duplicate studies, enables reuse across teams, supports meta-analysis of patterns across studies, and democratizes access to user knowledge.

What is atomic research in the context of research repositories?

An approach that breaks research into small reusable units — commonly experiments (studies), facts/observations (raw evidence), insights (interpretations), and recommendations/opportunities — each tagged and linked, so evidence can be traced, recombined, and searched across many studies rather than trapped inside single reports.

What is a user persona?

A fictional but evidence-based archetype representing a key user group, typically including a name, photo, demographics, goals, needs, behaviors, frustrations/pain points, and context of use. Personas build shared empathy and keep design decisions anchored to real user needs rather than the team's assumptions or edge cases.

What core elements should a research-based persona include?

Name and representative photo; brief bio and relevant demographics; goals and motivations; behaviors and usage context (technology, environment); needs; frustrations/pain points; and often a representative quote. Every element should trace back to research data, not invention.

Why is it recommended to create a small number of personas (roughly 3–5) per product?

Each persona should represent a behaviorally distinct user segment with different goals and needs. Too many personas dilute focus and become impossible for the team to remember and design for; too few can hide meaningfully different needs. A small set keeps design decisions sharp, often with one designated primary persona.

What is a proto-persona and how does it differ from a research-based persona?

A proto-persona is created quickly from the team's existing assumptions, domain knowledge, and available secondary data — without new primary research. A research-based persona is built from actual user research (interviews, field data, surveys). Proto-personas align team assumptions and are cheap but unvalidated; research-based personas are evidence-backed and reliable for major decisions.

When is it appropriate to use a proto-persona, and what must be done with it afterward?

Early on, when there is no time or budget for primary research — to make the team's assumptions explicit, align stakeholders, and guide initial direction. It must be treated as a hypothesis and validated (and corrected) with real user research as soon as possible, not used as lasting evidence.

What are the four quadrants of a classic empathy map?

Says — direct quotes and statements from the user; Thinks — beliefs and thoughts, including what they won't say aloud; Does — observed actions and behaviors; Feels — emotional states (worries, excitement, frustration). Extended versions add Pains and Gains, all centered on one user or persona.

What is the purpose of an empathy map, and when in the process is it typically used?

To organize research observations about a single user type into a shared, at-a-glance picture of their perspective — building team empathy, exposing gaps and contradictions in knowledge (e.g., says one thing, does another), and feeding persona creation. It is typically used early, in the empathize/define stages, right after gathering research.

What is the core idea of the Jobs To Be Done (JTBD) framework?

People don't buy products; they "hire" them to get a job done — to make progress in a specific circumstance. Research should focus on the underlying job (functional, plus emotional and social dimensions) rather than on demographics or product features. Classic illustration: people don't want a drill, they want the hole (and what the hole enables).

What is the standard job story template in the JTBD framework, and how does it differ from a user story?

Job story: "When [situation], I want to [motivation], so I can [expected outcome]." It replaces the user story's persona clause ("As a [user type], I want…") with the triggering situation/context, emphasizing circumstance and causality over user attributes.

What is a customer journey map and what are its typical components?

A visualization of the end-to-end experience a user (usually one persona) has while pursuing a goal over time. Typical components: the persona and scenario/goal; journey phases/stages; user actions at each stage; thoughts; emotions (often an emotion curve); touchpoints/channels; pain points; and opportunities for improvement.

Differentiate current-state and future-state journey maps, and journey maps from service blueprints.

A current-state journey map documents the experience as it exists today (best for finding pain points); a future-state map envisions the improved experience to align teams on a vision. A journey map shows the experience from the user's perspective, while a service blueprint extends it with the organization's backstage: frontstage/backstage employee actions, support processes, and systems that deliver each touchpoint.