Understanding Users and Task Analysis

Every interactive system is built on a set of assumptions about the people who will use it. When those assumptions are explicit, tested, and documented, they become a design asset; when they remain implicit, they become the single largest source of design failure. Designers are, almost by definition, atypical users: they know the system's internal structure, they have unlimited exposure to it, and they are unusually tolerant of complexity. The discipline of understanding users exists to counteract this bias, and task analysis exists to convert that understanding into a structured, inspectable description of work that can drive concrete interface decisions. This topic moves from who the users are, through how we learn about them, to how their goals and knowledge are represented, and finally to the formal decomposition techniques — Hierarchical Task Analysis and Cognitive Task Analysis — that turn observation into design specification.

Who Are the Users?

The phrase "the user" is dangerously singular. Real systems serve populations that vary along many dimensions simultaneously, and each dimension carries design consequences. Three of the most consequential are age, experience, and domain knowledge.

• Age affects perception, motor control, and expectation. Visual acuity and contrast sensitivity decline with age, which argues for larger default type sizes, higher contrast ratios, and avoidance of low-contrast grey-on-grey styling. Fine motor precision also declines, so touch targets that are comfortable for a twenty-year-old (perhaps 7 mm) may be frustrating for a seventy-five-year-old (who may need 10–12 mm with generous spacing). Age also correlates with different interaction conventions: a user who learned computing in the desktop era may look for a visible "Save" command, while a user raised on mobile apps may assume autosave and be disoriented by its absence.
• Experience operates on at least two axes: experience with computing in general, and experience with this particular system. A novice needs visible affordances, informative labels, and forgiving error recovery. An expert needs speed: keyboard shortcuts, batch operations, command palettes, and the ability to bypass confirmation dialogs. Good design does not choose between them; it layers accelerators on top of a discoverable base, so that the same interface supports a learning path from novice to expert. A classic example is a spreadsheet: a beginner uses the ribbon and the function wizard, while an expert types =XLOOKUP( directly and navigates with Ctrl+Arrow.
• Domain knowledge is distinct from computing experience and is frequently confused with it. A consultant radiologist may be a hesitant computer user but possesses deep, highly structured knowledge of anatomy and diagnostic workflow. For such users, the interface should speak the domain's language (correct terminology, conventional abbreviations, familiar units and orderings) and should not patronise them with explanations of domain concepts. Conversely, a member of the public using a health-portal booking system has no domain vocabulary at all and needs plain-language translation of clinical terms.

Beyond these, designers routinely profile users by cultural and linguistic background (reading direction, date and number formats, name structures), accessibility needs (screen-reader use, colour vision deficiency, motor impairment, cognitive load sensitivity), frequency of use (daily versus once a year — an annual tax filing system cannot rely on remembered procedure), and the physical and social context of use (a warehouse scanner used with gloves in cold storage; a point-of-sale terminal used under time pressure with a queue watching).

Because populations are heterogeneous, it is useful to classify users by their relationship to the system rather than only by their attributes. The conventional classification is primary, secondary, and tertiary.

The categorisation is not a ranking of importance — it is a reminder of coverage. A design optimised exclusively for primary users can impose severe costs elsewhere: an e-prescribing screen that lets a doctor enter an order in four keystrokes but produces an ambiguous printed label creates risk for the patient (tertiary) and rework for the pharmacist (secondary).

Stakeholder analysis extends this further by systematically enumerating everyone with an interest in the system, including those who never touch it, and recording what they want, what they fear, and what power they hold. A simple stakeholder map lists each party against their interest and influence. The technique is valuable precisely because it surfaces hidden users. Consider a self-service supermarket checkout: the obvious user is the shopper, but the store's loss-prevention staff interact with the exception-handling screens dozens of times an hour, the store manager needs throughput reporting, and the cleaning staff need the hardware to be wipeable. None of these appear in a naive "the user is the shopper" framing, yet the loss-prevention workflow determines whether the whole installation is tolerable to operate. Similarly, in a school attendance system, the hidden stakeholders include parents who receive automated absence texts and safeguarding officers who must be able to reconstruct a record months later. Hidden users are often discovered by asking three questions of any proposed feature: Who else sees the output? Who has to clean up when this goes wrong? Who is accountable if it is misused?

Methods for Researching User Needs

No single research method gives a complete picture, because methods differ along two important dimensions: whether they capture what people say or what people do, and whether they capture a moment or a period. Triangulating across these dimensions is standard practice.

Interviews and surveys are the workhorses of self-report. A well-run interview avoids leading questions ("Don't you find the current system slow?") in favour of concrete, episodic prompts ("Walk me through the last time you had to cancel an order — what happened?"). Episodic questions anchor the participant in a real event and dramatically reduce the tendency to produce idealised accounts. Interviews should probe frustrations and workarounds especially hard, because workarounds are fossil evidence of unmet needs: a sticky note of shortcut codes on a monitor bezel, a parallel spreadsheet maintained "because the system can't do it", or a shared login used to bypass a permissions model. Surveys complement interviews by establishing how widespread a theme is; the typical sequence is interview first to generate the right response options, then survey to measure them. Standardised instruments such as the System Usability Scale (ten Likert items yielding a 0–100 score) are useful for benchmarking, but they diagnose nothing on their own.

The fundamental limitation of all self-report is that people are unreliable narrators of their own behaviour. They forget routine steps precisely because those steps are automatic, they compress a messy twenty-minute process into a tidy four-step story, and they describe the policy rather than the practice.

Contextual inquiry addresses this by moving the research into the user's own environment and adopting a master–apprentice stance: the user works as usual while the researcher watches, asks small clarifying questions in the moment, and periodically reflects an interpretation back for correction. Four principles are usually cited — context (go where the work is), partnership (co-investigate rather than interrogate), interpretation (check your meaning-making with the user), and focus (steer towards relevant work without constraining it). Contextual inquiry reliably reveals things interviews cannot: that the dispatcher keeps three windows tiled and transcribes between them by hand; that the radio interrupts the task on average every ninety seconds; that the "required" field is universally filled with a dot because the real information is unavailable at that point in the process; that a laminated crib sheet encodes the organisation's actual procedure. Physical artefacts collected during a visit — forms, labels, printouts, annotated screenshots — are among the richest design inputs available.

Diary studies and usage logs extend observation across time. In a diary study, participants record entries themselves, triggered either by time (end of each shift) or by event (every time you need to look something up). Modern diary studies often use a phone app with photo and voice capture, lowering the burden of entry. The method is the only practical way to observe phenomena that are rare, private, or distributed — for example, how a family coordinates a care schedule over a month, or how a field engineer's connectivity varies between sites. Usage logs and analytics supply the objective counterpart: funnel analysis showing that 38% of users abandon at the address-verification step, event timing showing that the "advanced search" panel is opened by 2% of sessions, or error telemetry showing that a particular validation message fires 400 times a day. Logs excel at identifying where and how often something goes wrong but are mute about why; the productive pattern is to let analytics localise a problem and then use interviews or observation to explain it. All longitudinal collection raises ethical obligations: informed consent, clear statements of what is captured, data minimisation, anonymisation or pseudonymisation, and the right to withdraw.

User Goals and Mental Models

Research produces raw material; goals and mental models are the structures we use to make sense of it. A widely used framing (from goal-directed design) separates three levels of goal.

The distinction matters because the three levels fail in different ways. A system can satisfy an end goal while violating an experiential goal — the task completes, but the user feels harassed by interruptions, modal dialogs, and upsells — and such systems are abandoned as soon as an alternative exists. Conversely, a delightful interface that does not advance a real end goal is a novelty. Life goals are rarely addressed by individual screens but should discipline the roadmap: a budgeting app whose users' life goal is "stop feeling anxious about money" should think hard before adding a feature that sends daily spending alerts.

A mental model is the user's internal, working theory of how the system behaves — what its parts are, how they relate, what will happen if they do X. Mental models are typically incomplete, partly wrong, and heavily analogised from previous experience, yet they are what people reason with. The conceptual model is the designer's intended story of the system, communicated through the interface; the system image is everything the user can actually perceive (screens, labels, documentation, feedback). Breakdowns occur when the system image fails to convey the conceptual model, or when the conceptual model itself conflicts with the user's prior model.

Two techniques are especially useful for eliciting mental models:

• Card sorting probes how people categorise and relate concepts. Participants are given labelled cards (content items, features, document types) and asked to group them. In an open sort they create and name their own groups, revealing their native categories and vocabulary; in a closed sort they place cards into predefined categories, testing whether a proposed structure is guessable. Results are aggregated across participants — commonly as a similarity matrix or dendrogram showing which items were repeatedly grouped together. A university website open sort might reveal that students think in terms of "things I have to do this week" (fees, timetable, assignment submission) while staff think departmentally; this directly suggests task-based rather than org-chart-based navigation. The complementary technique, tree testing, takes a proposed hierarchy and asks users to locate items in it, measuring success and directness.
• Think-aloud protocol asks a user to verbalise thoughts continuously while performing a task. Because the verbalisations expose expectations and predictions ("I'll click Settings — that's probably where my saved cards are… no, it isn't, maybe Account…"), the transcript is effectively a trace of the mental model being applied and repaired. Concurrent think-aloud gives in-the-moment data but can slightly distort performance timings; retrospective think-aloud (replaying a screen recording and narrating afterwards) preserves natural performance at the cost of some recall accuracy. Prompts should be neutral — "What are you thinking?" — never "Why didn't you see the button?"

Other elicitation devices include asking users to draw the system ("sketch where your files live"), probing predictions before action ("what do you expect will happen when you press this?"), and analysing spontaneous metaphors in interview transcripts ("my inbox is a to-do list", "the cloud is like a big hard drive somewhere").

Design then has three ways to bridge the gap between the user's model and the system's structure. First and best, change the system so its structure matches the user's: if users think of email as conversations, build conversation threading rather than teaching them about message IDs. Second, change the system image so that the real structure is visible and comprehensible — clear labels in the users' own words, visible state, immediate feedback, constraints that make invalid actions impossible, and metaphors that carry the right implications. Third and last, change the user's model through onboarding, in-context help, and training — which is slow, expensive, and leaks as staff turn over, and should therefore be used only where the underlying complexity is irreducible. Mismatches that are never bridged produce predictable, repeatable errors: thermostats set to 30°C in the mistaken belief that a higher setting heats faster (a valve-versus-thermostat model confusion), files "lost" because a user's model of cloud sync does not include a local/remote distinction, or repeated double-submission of a payment because the system shows no evidence that the first press registered.

Introduction to Task Analysis

Task analysis is the systematic study of what people do to achieve a goal: the sequence of actions, the decisions taken, the information required at each point, the artefacts and tools used, and the conditions under which the work varies. Where user research tells you who people are and what they want, task analysis tells you in detail how the work actually proceeds — and it does so in a representation that can be reviewed, argued about, and handed to a design team.

A thorough task analysis typically captures, for each step: the trigger that initiates it; the actor responsible; the information needed and its source; the action performed; the output or state change produced; the decision rules applied; the typical duration and frequency; and the known failure modes. Consider a seemingly trivial task — a warehouse operative picking an item for an order. A surface description is "scan the item and put it in the tote". A task analysis reveals that the operative must first locate the bin from a code, verify that the bin label matches the pick list, check the item's expiry date against a policy threshold, count a multi-pack quantity, decide what to do if the bin is short (substitute, partial-pick, or raise an exception), and record the result — all while holding a scanner and pushing a cart. Each of those hidden decisions is a design requirement.

Task analysis is also a diagnostic instrument. Laying out the current ("as-is") process almost always exposes inefficiencies and gaps:

• Redundant data entry — the same reference number typed into three systems, which indicates an integration opportunity and a likely source of transcription errors.
• Context switching — steps that require leaving the application to consult a spreadsheet, a laminated chart, or a colleague, each of which is a candidate for bringing information into the interface.
• Memory bridges — information carried in the head from one screen to the next (an account number, a quoted price) which should instead be carried by the system and displayed where it is needed.
• Waiting and blocking — steps that stall pending another person's action, suggesting notification, queueing, or parallelisation.
• Unsupported exceptions — the "what if" branches that users handle by phone, email, or informal workaround because the system has no path for them. These are frequently 20–30% of real volume while being 0% of the original specification.

The outputs feed directly into three design artefacts. Information architecture follows from what information is needed when and how users group it (reinforced by card-sorting data). Navigation design follows from the sequences and branches: steps performed in strict order suit a wizard; steps performed in varying order by experienced users suit a dashboard with independently addressable sections; frequently alternated pairs of steps should not be separated by deep navigation. Feature prioritisation follows from frequency and criticality: a step performed two hundred times a shift deserves a dedicated single-keystroke affordance, while a step performed twice a year can live three clicks deep in a menu with explanatory text.

Hierarchical Task Analysis (HTA)

HTA is the most widely used task-analysis notation. It represents work as a hierarchy: a single top-level goal is decomposed into sub-goals, which are decomposed further until the analyst reaches operations — units of behaviour concrete enough to be designed for and observed. Crucially, each level of the hierarchy also carries a plan: a statement of the conditions under which its subtasks are performed and in what order. Without plans an HTA is merely a list; with them it is an executable description of work.

Decomposition is conventionally numbered so that any node can be referenced unambiguously. A worked example for withdrawing cash at an ATM:

0. Withdraw cash from ATM
   Plan 0: do 1 - 2 - 3 - 4 - 5 - 6 in order.
           If card is retained at any point, go to 7 and exit.

   1. Initiate session
      Plan 1: do 1.1; then 1.2.
      1.1 Insert card
      1.2 Enter PIN
          Plan 1.2: do 1.2.1 then 1.2.2.
                    If "incorrect PIN", repeat up to 3 times;
                    on 3rd failure, exit (card retained).
          1.2.1 Read keypad / shield keypad
          1.2.2 Key digits and confirm

   2. Select "Withdraw cash"

   3. Specify amount
      Plan 3: if required amount appears on the quick-select
              list, do 3.1; otherwise do 3.2 then 3.3.
      3.1 Press quick-select amount
      3.2 Select "Other amount"
      3.3 Key amount and confirm

   4. Choose receipt option

   5. Retrieve card

   6. Retrieve cash

   7. Record retained card / contact bank

Several things are worth noticing in this small example. The hierarchy separates what is achieved (sub-goal 3, "specify amount") from how it may be achieved (3.1 versus 3.2–3.3), so the analysis remains valid even if the interface changes. Plans capture three distinct kinds of logic: sequence (plan 0), selection under a condition (plan 3), and iteration with a bound (plan 1.2). Finally, the ordering of 5 and 6 is itself a design decision with safety consequences — ATMs were redesigned to dispense the card before the cash precisely because task analysis and field data showed a post-completion error: once the user's main goal (cash) was satisfied, the subsidiary step of retrieving the card was systematically forgotten.

Practical guidance for building an HTA:

• Start from a goal, not a screen. "Book a repeat prescription" is a goal; "use the prescriptions page" is not. Goal-framing keeps the analysis independent of any particular implementation, which is what allows it to inform redesign.
• Use a consistent stopping rule. A common one is the P × C rule: stop decomposing when the probability of failure multiplied by the cost of failure is acceptably low. A step like "key amount and confirm" need not be broken into individual keystrokes unless mis-keying is both likely and costly — in an infusion-pump dose entry, it certainly is, and decomposition continues to the level of individual digit entry and decimal-point placement.
• Keep levels balanced and verb-phrased. Each node should be a verb phrase ("verify patient identity"), and sibling nodes should sit at roughly the same granularity; a level that mixes "authenticate user" with "press Enter" is a sign of uneven analysis.
• Validate with real practitioners. An HTA built only from documentation describes the official procedure, which is reliably different from the work as actually done. Walking the diagram through with experienced users surfaces the missing branches.

HTA earns its keep in three ways. It identifies training requirements, because any node whose performance depends on knowledge not available in the interface is, by definition, something a person must be taught or given a job aid for. It identifies error-prone steps, especially when each leaf is examined against a taxonomy of error types — omission, commission, wrong order, wrong object, timing, substitution. Steps that are subsidiary to the main goal (removing the original from a photocopier, retrieving a card), steps that require carrying information in memory, and steps that look similar but have different consequences (two adjacent buttons labelled "Submit" and "Submit and close") are predictably hazardous. And it identifies automation opportunities: nodes that are purely mechanical, deterministic, and information-complete — recalculating a total, looking up a postcode, copying a reference number between fields — are candidates for the system to perform, while nodes requiring judgement under uncertainty generally are not. A disciplined question at each leaf is: must a human do this, should a human do this, or is the human here only because the software is not?

Cognitive Task Analysis

HTA describes observable behaviour very well and the thinking behind it only weakly. In knowledge-intensive domains — air traffic control, emergency medicine, fraud investigation, network operations, military command — the hard part of the task is not the sequence of button presses but the perception, judgement, and anticipation that determine which action is taken at all. Cognitive Task Analysis (CTA) is a family of methods for making that invisible work explicit: the goals that are actually being pursued, the cues that experts attend to, the mental models and strategies they bring, and the trade-offs they make under time pressure, ambiguity, and risk.

The central difficulty is that expertise is largely tacit. Skilled performers have automated much of their behaviour and genuinely cannot report it; asked how they knew a patient was deteriorating, an experienced nurse may say "she just looked wrong". CTA methods are designed to work around this:

• Critical Decision Method (CDM). A structured retrospective interview organised around a single memorable, non-routine incident. The interviewer sweeps the story several times: first to establish the timeline, then to identify the decision points, then to probe each decision with specific question types — What cues did you notice? What did you expect to happen next? What alternatives did you consider and reject, and why? What would a less experienced colleague have done here? What information would have made this easier? What if the patient had also been hypotensive? The counterfactual probes are especially productive because they force comparison and thereby expose the decision rule.
• Knowledge audit. A targeted interview sweeping known dimensions of expertise: noticing subtle cues, spotting anomalies, improvising, recognising the "big picture", anticipating consequences, and knowing when the standard procedure does not apply. Each probe asks for a concrete example plus the cues and strategies involved.
• Concurrent think-aloud on representative problems, often supported by eye tracking to show what was actually looked at and in what order.
• Simulation and scenario walkthroughs, in which a realistic case is presented and paused at decision points so the expert can articulate assessments and predictions.
• Comparative expert–novice analysis, which isolates what expertise actually consists of by contrasting how the two groups structure the same problem.

The distinction between HTA and CTA is one of focus rather than rivalry; in practice CTA is often used to "deepen" particular nodes of an HTA that are known to be judgement-heavy.

The characteristic deliverable of CTA is a decision requirements table, which converts tacit expertise into explicit display requirements. For an intensive-care deterioration-monitoring display, one row might look like this:

That final column is the point of CTA for designers. Expert knowledge that has been made explicit can be surfaced in the interface so that less experienced users are supported: trend sparklines instead of bare numbers, anomaly highlighting that draws attention to the cue an expert would have noticed, contextual decision aids that show the alternatives an expert would have considered, comparative baselines, and worked rationale ("flagged because respiratory rate has risen 6/min in 2 hours"). The ethical and practical caveat is that such aids should support judgement rather than replace it: over-automated alerts breed alarm fatigue, and displays that hide their reasoning erode the user's ability to detect when the system is wrong.

Applying Task Analysis to User-Centered Design

Task analysis only matters if it changes the artefact. The translation happens through three linked practices: deriving design blueprints, prioritising effort, and validating designs back against the analysis.

First, task flows and use cases turn the hierarchy into an interaction specification. A task flow is a linear or branching diagram of the steps a user takes through a proposed system, including decision diamonds and error paths; a use case is a textual description with actors, preconditions, a main success scenario, and extensions. An HTA plan maps onto these almost mechanically: a strict sequence plan suggests a stepped wizard with a visible progress indicator; a selection plan becomes a branch that the interface should make explicit and reversible; an iteration plan becomes a repeatable block with an obvious "add another" affordance and a clear termination condition. A worked fragment for an insurance claim:

Use case: Submit a motor claim (main success scenario)
  Actor: Policyholder (primary)   Secondary: Claims handler
  Precondition: Policy is active; incident occurred within 30 days

  1. User identifies the policy                [from HTA 1.1-1.2]
  2. System confirms cover and shows excess    [info need: excess amount]
  3. User describes incident (date, location, parties)
  4. User uploads photographs                  [iteration: 1..n images]
  5. System validates required evidence
  6. User reviews summary and submits
  7. System issues claim reference and expected contact time

  Extension 4a: No photographs available
     4a1. User selects "add later"; system issues reference
          and sends a secure upload link valid for 7 days.
  Extension 2a: Cover excludes incident type
     2a1. System explains exclusion in plain language and
          offers to connect to an adviser.

Notice that the extensions come straight from the "unsupported exceptions" found in the as-is analysis — the branches that users previously resolved by telephone. Designing them in is usually where the largest usability gains are found.

Second, prioritisation. Not all tasks deserve equal design investment, and task analysis supplies the three variables needed to allocate it rationally: frequency (how often the task occurs), criticality (the cost of failure), and error-proneness (how likely failure is). Combining them gives a defensible ranking and, importantly, different kinds of treatment rather than simply "more effort".

Third, validation. Because the task analysis is written independently of any particular design, it becomes a test oracle for prototypes. Three complementary checks are standard:

• Coverage check. Walk every leaf of the HTA against the prototype and ask where each one is performed. Any leaf with no home in the interface is a gap; any screen element that corresponds to no leaf is either an unexamined assumption or scope creep.
• Step-count and effort comparison. Compare the as-is and to-be flows for the highest-frequency tasks. If the new design adds steps to a task performed three hundred times a day in order to save steps on a task performed weekly, the trade is almost certainly wrong — and task analysis makes that visible before code is written. This is the standard defence against a redesign that is prettier but slower.
• Scenario-based testing. Build usability test tasks and cognitive walkthrough scenarios directly from the analysis, including the exception branches and the decision points identified by CTA. Testing should include realistic conditions revealed by contextual inquiry — interruptions, incomplete data, time pressure — because a design that works only on clean data in a quiet room has not been validated against the real task.

Two cautions complete the picture. Task analysis describes work as it is currently done, so uncritical fidelity to the as-is process can cement obsolete constraints — the analyst must distinguish steps that exist because the domain demands them from steps that exist because the old system demanded them. And work evolves: an analysis is a living document that should be revisited as the system changes the very behaviour it described. Used with that awareness, task analysis is what keeps user-centred design honest — it ensures the system supports the way people actually work rather than quietly imposing new burdens on them in the name of improvement.

Alignment with Learning Outcomes

• MO1 — Partially supported. The topic does not enumerate the types of human memory or their capacities, but it touches the same constraint from the task side: "memory bridges" (information users must carry in their heads between steps), post-completion errors, and infrequent tasks that cannot rely on remembered procedure all illustrate why memory limits must shape interface design.
• MO2 — Directly supported. The treatment of mental models, conceptual models and the system image, the elicitation techniques (card sorting, think-aloud), and the worked examples of mismatch-driven errors (thermostat settings, cloud sync, double payment submission) explain how a gap between the user's model and system behaviour produces systematic, predictable error.
• MO3 — Directly supported. The HTA section provides the goal–sub-goal–operation decomposition, numbered notation, plans expressing sequence, selection and iteration, stopping rules such as P × C, and a worked ATM example, then shows how the resulting hierarchy drives navigation structure, shortcuts, confirmations and automation decisions.
• MO4 — Partially supported. The topic does not compare usability inspection methods with user testing as such, but it supplies the research-method comparison (interviews, surveys, contextual inquiry, diary studies, logs) and shows how task analysis generates the scenarios and coverage checks used in both cognitive walkthrough and scenario-based user testing, which is the input that later evaluation choices depend on.
• MO5 — Partially supported. Prototyping itself is not taught here, but the topic supplies what a prototype must embody and be tested against: task flows and use cases as interface blueprints, frequency/criticality-based prioritisation of what the prototype should foreground, and validation of prototypes against task-analysis findings.

Before you can design an interface, you have to know who will use it, what they are trying to accomplish, and how they think the system works. This topic covers the front end of user-centered design: identifying your actual users (as opposed to the imagined "average person"), researching their needs through interviews, observation, surveys, and contextual methods, and modeling their goals and mental models so that your design matches their expectations rather than the developer's internal logic.

The second half of the topic turns research into structure. Task analysis is the practice of breaking down what users do into describable units: Hierarchical Task Analysis (HTA) decomposes a high-level goal into subtasks and plans that govern their order, while Cognitive Task Analysis focuses on the knowledge, decisions, and attention demands hidden behind observable actions. Both feed directly into design decisions — what to put on a screen, what to automate, what to group together, and where errors are likely.

The videos below focus on the research methods side of this topic. As you watch, pay attention to three things: what kind of data each method produces (what people say versus what people do), what context the data is gathered in, and how a researcher avoids leading or biasing the participant. Try to connect each method back to how you would use its output to build an HTA or a mental-model description for a system you are designing.

NNgroup · 3:01 · 19,608 views · 4y ago · captions — Watch on YouTube

About this video

A short overview of contextual inquiry, a research method in which the researcher observes and questions users in their own working environment while they perform real tasks. Based on its title and description, it explains what contextual inquiry is used for — defining requirements, improving processes, and learning what matters to users — and when it is an appropriate choice. Expect a compact introduction rather than a step-by-step field guide.

Why this video

Nielsen Norman Group is a well-established UX research organization, and this video maps directly onto the "Methods for Researching User Needs" subtopic; contextual inquiry is also the classic source of raw material for task analysis, since it captures real tasks in real settings.

UX in the Head · 11:26 · 35,698 views · 3y ago · captions — Watch on YouTube

About this video

A broader survey of the UX research methods you are most likely to encounter, including common techniques and several lesser-known ones, with discussion of what each is used for. At about eleven minutes it provides enough room to compare methods rather than just name them, which helps you see how interviews, observation, surveys, and testing differ in the kind of evidence they produce. Use it to build a mental catalog of options you can draw on later.

Why this video

It gives you the comparative overview that the "Methods for Researching User Needs" subtopic requires, so you can justify choosing one method over another instead of defaulting to whichever one you learned first.

NNgroup · 3:42 · 18,210 views · 3y ago · captions — Watch on YouTube

About this video

A practical guide to the think-aloud protocol: how to explain the technique to participants, why verbalizing while working is useful, and how to prompt people who fall silent without putting words in their mouths. The focus is on facilitation skill — reinforcing the behavior and keeping the session natural rather than scripted.

Why this video

Think-aloud is the primary way researchers get access to a user's mental model and decision-making, which makes it the best bridge between the research methods subtopics and both "User Goals and Mental Models" and "Cognitive Task Analysis."

These videos support the module outcomes mainly by supplying the evidence-gathering skills that the analysis and evaluation outcomes depend on. Contextual inquiry and think-aloud sessions are how you surface a user's mental model and detect where it diverges from system behavior (MO2), and the verbalized reasoning and observed task sequences they produce are the raw input you decompose into goals, subtasks, and plans when performing a Hierarchical Task Analysis (MO3). The survey of UX research methods gives you the comparative vocabulary needed to argue why one technique fits a given design context better than another, which carries directly into selecting between expert-driven inspection and user testing later in the module (MO4), while the findings from all three feed the design and prototyping decisions you will make in MO1 and MO5.

These videos were chosen from YouTube on October 3, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.