Every interface you design is used by a person with a limited and predictable set of cognitive abilities. We perceive only part of what is in front of us, we attend to even less, and we can hold only a handful of items in mind at once before they decay or get overwritten. Theories of human information processing — perception, attention, working memory, long-term memory, and the time course of feedback — give us a vocabulary for explaining why some interfaces feel effortless and others feel exhausting.
In this course, these ideas are the justification behind many design rules you will otherwise have to memorize. Recognition over recall, chunking, consistent placement, visible system status, and short feedback latencies are not style preferences; they are responses to documented limits in human cognition. Understanding the underlying mechanism lets you reason about novel design situations instead of guessing.
The videos below come from the Nielsen Norman Group, a long-established usability research and consulting firm. As you watch, note two things: first, the specific claim about human capability being made (how much, for how long, under what conditions), and second, the concrete interface implication drawn from it. Also notice how the third video shifts from describing cognition to using a cognitive model as an evaluation tool — that bridge from theory to method is exactly what we are after.
A short explainer on short-term memory: how little information people can hold at one time and how quickly it fades when attention shifts. The video connects these limits to common interface decisions, such as not forcing users to carry information from one screen or step to the next. Expect practical examples of designs that respect — or violate — these constraints.
It directly addresses the memory subtopic with concrete capacity and duration claims, and the Nielsen Norman Group is a credible source for research-grounded usability guidance stated in plain language.
This video distinguishes working memory — the limited mental workspace used while performing a task — from external memory, meaning information the interface itself displays or stores on the user's behalf. It argues that interfaces should offload remembering onto the screen rather than onto the user. Likely examples include keeping instructions, entered values, or prior selections visible during multi-step tasks.
It extends the memory subtopic beyond raw capacity into a design strategy, which is the key move for this course: treating the interface as an extension of the user's cognitive system.
An introduction to the cognitive walkthrough, a task-based usability inspection method in which evaluators step through a task as a first-time user would and ask whether the user would know what to do and whether they would recognize that their action was correct. The video explains the purpose of the technique, its focus on learnability, and how it differs from other evaluation approaches. Expect a walkthrough of the questions evaluators ask at each step.
It shows cognition applied as an analytical lens — the method is built directly on assumptions about perception, attention, and feedback — and it connects this topic to the evaluation methods covered later in the module.
The first two videos support MO1 by distinguishing short-term and working memory, naming their practical capacity limits, and translating those limits into interface constraints such as minimizing recall and externalizing information on screen. The cognitive walkthrough video supports MO2, since the method's core questions surface exactly where a user's expectation diverges from what the system actually does, and it supports MO4 by illustrating a structured, expert-driven inspection technique you can later compare against empirical user testing. Together they supply the cognitive rationale you will draw on when decomposing tasks (MO3) and when justifying the layout, labeling, and feedback choices in your prototype (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.
Every time you sit down in front of an unfamiliar app, you are not starting from zero. You bring prior knowledge: general world knowledge, knowledge of similar software, knowledge of conventions (a trash can deletes, an underlined blue word links), and procedural habits built from years of clicking, tapping, and swiping. From this knowledge you assemble a mental model — an internal, often incomplete and sometimes wrong, account of what the system contains and how it behaves. That model is what you use to predict what a button will do before you press it.
Designers cannot hand users a mental model directly. What they can do is express a conceptual model through the interface: the objects, actions, relationships, and metaphors the design makes visible. Interface metaphors (desktops, folders, carts, feeds) act as cognitive bridges, letting users borrow structure from something familiar. When the conceptual model communicated by the interface matches the system's real behavior, users form accurate mental models quickly; when it does not, they form confident but wrong models and make systematic, repeatable errors.
As you watch these videos, look for three things. First, what cues in the interface are doing the model-building work — spatial arrangement, direct manipulation of visible objects, labels, animation, explanations. Second, how mental models change over time as users accumulate experience with a system. Third, how early impressions can distort a model in ways that persist, which is a direct design concern rather than just a psychological curiosity.
A short Nielsen Norman Group explainer on direct manipulation, the interaction style in which users act on visible depictions of objects rather than issuing commands about them. Expect a definition, examples from graphical interfaces such as dragging, resizing, and moving on-screen objects, and discussion of why acting directly on a representation feels natural. The video is likely to touch on the trade-offs and limits of the approach as well.
Direct manipulation is the clearest practical case of an interface metaphor working as a cognitive bridge: the screen objects stand in for real objects, so users can reuse everyday physical knowledge. NN/g is a long-established usability research and training organization, and the presentation is concise and example-driven, making it a good anchor for the metaphor and conceptual-model subtopics.
A SIGCHI research presentation on how anchoring bias — being overly influenced by information encountered first — shapes the mental models users form of an AI system and how much they then rely on it. The talk reports on a study of explainable AI, examining how the order and nature of early examples affect users' understanding and trust. Expect study design, findings, and implications for designing explanations.
This video shows mental models forming and evolving through interaction, and shows how a distorted model leads to miscalibrated reliance and systematic error. It extends the topic into a current, high-stakes design context where the gap between a user's model and a system's actual behavior has clear consequences.
These videos focus most directly on MO2: the SIGCHI talks show how mental models are formed, how they can be skewed by early information or by interface form, and how a mismatch between a user's model and the system's real behavior leads to predictable errors and misplaced reliance. The direct manipulation video supplies the design-side counterpart, illustrating how a well-chosen metaphor and visible conceptual model reduce the memory and reasoning burden on users, which connects to the memory and capacity constraints discussed in MO1. The design implications raised across all three — making structure visible, sequencing what users see first, matching form to expectation — also feed into the later module outcomes on task decomposition (MO3), evaluation (MO4), and prototyping (MO5), where you will need to justify design choices in terms of the understanding they produce in users.
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.
People make mistakes, and interactive systems are where many of those mistakes become visible. This topic looks at human error systematically: the difference between slips (doing the wrong thing while intending the right thing) and mistakes (intending the wrong thing in the first place), the conditions that make errors more likely, and the design choices that either invite or prevent them. A recurring theme is that error is rarely just "user fault" — poorly labelled controls, ambiguous feedback, and interfaces that conflict with a user's mental model all produce predictable, systematic failures.
The second half of the topic turns to learning and recovery. Good interfaces are forgiving: they constrain dangerous actions, make the current state visible, offer undo, and explain problems in language the user can act on. They also support a learning curve, letting novices succeed through guidance and discoverability while giving experts faster paths. These goals sometimes pull against each other, which is why designers need principles rather than one-size-fits-all rules.
As you watch the three videos below, keep a running list of two things: (1) the specific error type or cause each example illustrates, and (2) the concrete design move used to prevent, contain, or recover from it. Pay attention to how error handling differs between a form on a large screen and an alert on a small mobile screen — the same principle often requires a different implementation.
An overview of what counts as "error" in human-computer interaction and how errors are classified, likely covering distinctions such as slips versus mistakes and the conditions under which each tends to occur. Expect definitions, examples from interactive systems, and discussion of why error classification matters for designers rather than being just academic labelling.
It directly addresses the foundational subtopics — types of human error and their causes — from the Interaction Design Foundation, a well-established HCI education producer, giving you shared vocabulary for the rest of the topic.
A practice-oriented talk on form design that focuses on preventing error messages before they happen, rather than writing better error text after the fact. It is likely to cover input formatting, labelling, validation timing, and other choices that reduce the chance a user enters something the system rejects.
Forms are the classic site of user error, so this video grounds the abstract idea of error prevention in concrete, reusable interface decisions you can apply in your own prototypes.
A short Nielsen Norman Group piece on how error alerts behave on mobile devices, arguing that modal dialogs for error messages often create usability problems on small screens. It discusses how error handling should respect the device context and the user's current task rather than interrupting it.
NNgroup is a leading usability research organisation, and this clip extends the topic into forgiving interfaces and error recovery while highlighting how platform context changes the right answer.
Together these videos support the module outcomes by making the link between user cognition and interface failure explicit. The first video supplies the error taxonomy needed to explain how a mismatch between a user's mental model and actual system behaviour produces systematic, repeatable errors rather than random ones (MO2), and connects to memory and attention limits that constrain what an interface can reasonably ask of users (MO1). The form-design and mobile-alert videos then translate that understanding into design practice — prevention, clear feedback, and recovery — giving you evaluable criteria to apply when critiquing a design (MO4) and concrete patterns to incorporate when you build your own low- to mid-fidelity prototype for a desktop or mobile context (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.
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.
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.
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.
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.
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.
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.
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.
Interaction design is the practice of shaping how people and interactive systems communicate with each other: what the system shows, what the user can do, what happens next, and how the user knows it happened. It sits between understanding users (goals, tasks, mental models) and building software, and it is deliberately iterative — you sketch a concept, put it in front of someone, learn what breaks, and revise.
In this course the topic matters for two reasons. First, the core principles (visibility, feedback, constraints, consistency, affordances and signifiers) give you a shared vocabulary for critiquing desktop and mobile interfaces instead of calling them simply "good" or "bad." Second, prototyping is how those principles get tested cheaply: paper sketches, wireframes, clickable mockups and higher-fidelity mockups each trade effort for realism, and each is useful at a different stage. A prototype is also a communication artifact — it lets you show a teammate, client or test participant an idea that words alone would not pin down.
As you watch, keep a running list of three things: concrete examples of designs that succeed or fail and the principle that explains why; the stages of the design process and where prototypes and user feedback enter the loop; and the difference between what an interface affords and what it merely appears to afford. You will use all three when you build your own low-to-mid fidelity prototype.
An introductory lecture defining interaction design as a field and distinguishing it from related areas such as HCI, UX and usability. Based on its title and chapter structure, it works through examples of poor design and references classic HCI material, including the Xerox PARC copier ethnography study, to show why designers must understand users in context. Expect coverage of foundational goals and principles that interaction designers work toward.
It is the natural starting point for the topic, establishing the vocabulary and motivating examples that the rest of the module builds on, and it is part of a structured HCI lecture series rather than a one-off clip.
The follow-up lecture in the same series, focused on the interaction design process itself. Its description points to the Double Diamond framework from the Design Council and the Design Squiggle, both of which illustrate how design moves between divergent exploration and convergent decision-making. Expect discussion of the activities involved — establishing requirements, designing alternatives, prototyping and evaluating — and how they repeat rather than run once.
This video directly addresses iterative design and where prototyping sits within the larger process, which is the conceptual backbone of the prototyping subtopics in this module.
A short, focused explainer from the Interaction Design Foundation on affordances: what an object or interface element communicates about how it can be used. The video explains the concept through a worked example and connects it to designing apps that users can operate without instruction. Expect brief treatment of how perceived affordances and visual signifiers guide action.
Affordances are one of the core interaction design principles you will be expected to apply in a prototype, and IxDF is a well-established UX education producer, so this gives a concise and reliable treatment of a single high-value concept.
These videos mainly support MO5: the first two establish the interaction design principles and the iterative, prototype-driven process you will follow when constructing a low-to-mid fidelity prototype for a desktop or mobile context, while the affordances video supplies a specific principle your prototype should demonstrate. They also reinforce MO2, since examples of bad design and the discussion of affordances both illustrate what happens when a system's behavior does not match the model a user forms from its appearance. The process lecture further connects to MO4 by showing where evaluation and user feedback belong in the design cycle, setting up the comparison between expert-driven methods and user testing covered elsewhere in the module.
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.
Usability evaluation is the part of the design process where you stop guessing and start checking. Instead of assuming an interface works, you apply structured methods — expert inspections, heuristic reviews, and tests with real users — to find out where people hesitate, make errors, or give up. The goal is not to prove a design is good, but to surface problems early enough and cheaply enough that you can fix them.
In this course, this topic ties together everything that comes before it. You have studied memory limits, mental models, and task structure; evaluation is how you verify that your design actually respects those constraints. You will also see two broad families of methods: expert-driven inspection (such as heuristic evaluation), which is fast and cheap but filtered through a reviewer's judgment, and user testing, which is slower but reveals behavior you could not have predicted. Knowing when to use each is a core professional skill.
As you watch, pay attention to three things: how usability is defined and broken into measurable components, how evaluators move from scattered observations to a consolidated, documented list of issues, and how qualitative findings get paired with quantitative metrics. Also notice that evaluation is described as something you repeat — each round feeds a design revision, which then gets tested again.
A short foundational explainer from Nielsen Norman Group that defines usability and breaks it into five quality components: learnability, efficiency, memorability, errors, and satisfaction. It frames usability as a property of the interface that can be assessed rather than a vague judgment of quality, and likely distinguishes usability from related ideas such as utility and overall user experience.
This is the natural starting point for the topic, giving you the vocabulary and the component framework that every later evaluation method measures against; NN/g is the field's most widely cited practitioner source.
A step-by-step walkthrough of how to run a heuristic evaluation, the best-known structured expert inspection method. Based on its chapter markers, it covers having evaluators review the interface independently, works through an example of applying heuristics to a real interface, and then shows how multiple evaluators' findings are consolidated into a single issue list before summarizing the process.
It directly addresses the structured evaluation and issue-documentation subtopics, and the consolidation step shows concretely how expert-driven methods differ in workflow and output from user testing.
An introduction to the System Usability Scale, a standardized ten-question survey given to participants at the end of a user test. The video explains what the instrument measures — perceived usability — and, based on its framing, how to administer it and interpret the resulting score in context rather than as an absolute verdict.
It covers the metrics and measures subtopic with a specific, widely used instrument, and demonstrates how a quantitative score can complement the qualitative issues you observe during user testing.
These three videos support MO4 most directly: together they let you compare a structured, expert-driven method (heuristic evaluation) with user-testing approaches and their associated measures (SUS), so you can reason about which fits a given design context, budget, and stage of development. The usability components video supplies the criteria — learnability, efficiency, memorability, errors, satisfaction — that connect evaluation back to the cognitive constraints and mental-model mismatches studied earlier in the module (MO1, MO2), since errors and learnability problems are often symptoms of those mismatches. The consolidation and scoring practices shown here also feed forward into prototyping work (MO5), where findings from one round of evaluation become the justification for the next revision of your low- to mid-fidelity prototype.
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.
Human factors (also called ergonomics) is the study of how people actually perform within systems — physically, perceptually, and cognitively — and how equipment, displays, software, and work processes should be shaped around those human capabilities and limits. In this topic we look at both halves of the field: the physical dimension (posture, reach, repetitive strain, workstation layout) and the cognitive dimension (attention, memory load, situational awareness, decision making). Both matter to interface design, because an interface is never used in isolation; it sits inside a task, a workstation, and a work system.
The topic also covers automation: what happens when part of a task is handed off to a machine or algorithm. Automation rarely removes work — it changes it, often shifting the human into a monitoring role, which can degrade situational awareness, create new error modes, and leave people out of the loop when something goes wrong. Display design principles (making relevant information visible, grouping it sensibly, matching it to the operator's mental model) are the main lever designers have for keeping people informed and in control.
While watching the two videos below, keep two questions in mind. First, what is the limiting factor in the scenario being described — the body, the senses, attention, or memory? Second, what design or work-system change would relieve that limit, and what new problem might that change introduce? That trade-off thinking is the core habit of human factors work.
A short workplace-safety style introduction to ergonomics, focused on body mechanics, posture, lifting, and workstation setup as ways to prevent injury. Based on its title and description, it covers practical, everyday ergonomic adjustments rather than theory, and frames ergonomics as fitting the task and equipment to the worker.
It gives a concise, concrete entry point to the physical dimension of ergonomics from a safety-training producer, which grounds the abstract idea of 'fitting the system to the human' before we extend that same logic to cognitive and interface concerns.
A TED talk by researcher Advait Sarkar arguing that offloading reasoning to AI chatbots can speed up work while quietly eroding the user's own critical thinking. Expect discussion of what is gained and lost when a cognitive task is automated, and how people's engagement with a task changes when a tool does part of the thinking.
It is a current, well-produced treatment of automation's effect on the human operator — complacency, loss of engagement, and out-of-the-loop effects — which maps directly onto the automation, human error, mental workload, and situational awareness subtopics.
These videos support the module outcomes by supplying the human-performance context the outcomes assume. The AI talk illustrates how a user's expectations about a system's competence can diverge from what it actually does, which is exactly the mental-model mismatch that produces systematic errors (MO2), and its discussion of cognitive offloading connects to memory and attention limits that constrain interface design (MO1). The ergonomics video reinforces task- and workstation-level thinking that feeds into decomposing goals into subtasks and evaluating designs in realistic use contexts (MO3, MO4). Together they give you criteria — physical fit, workload, error potential, and retained user control — to justify choices when you build and critique a prototype (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.
Most of this course treats interaction as a conversation between one person and one system. This topic widens the frame. Computer-Supported Cooperative Work (CSCW) studies how groups of people coordinate, communicate, and share artifacts through technology, and groupware frameworks are the tools built to support that work: shared documents, version control, notebooks, messaging, awareness cues, and handoff mechanisms. Alongside CSCW, this topic covers advanced interface paradigms — immersive and spatial displays, mixed reality, mobile and embedded interaction — plus the question of how much a system should automate and how much the human should oversee.
These ideas matter here because the design principles you have been learning do not disappear when the context gets more complex; they get harder to apply. Shared workspaces multiply the number of mental models in play, immersive displays change what information can be presented and how attention is divided, and automation shifts the user's role from doing to monitoring. Advanced technology does not excuse bad task analysis, confusing feedback, or untested prototypes — it raises the cost of getting them wrong.
The three videos below are short conference presentations from ACM SIGCHI, the professional community that produces much of the research in this field. As you watch, listen for how each team defines the collaborative setting, what methods they used to study it, and what design implications they draw. Pay attention to the vocabulary too — terms like teamwork versus taskwork, awareness, workflow, and research agenda are standard in this literature and will help you read further on your own.
A short CSCW '20 presentation from Dakuo Wang of IBM Research on how data science workers actually collaborate in practice. Based on the title and venue, it likely describes the distinct roles people occupy on data science teams, the workflows that connect them, and the tools they use to hand off work and stay coordinated. Expect an empirical, interview- or survey-based framing rather than a tool demo.
It is a direct, accessible entry point to CSCW and groupware: real teams, real handoffs, and the gaps between the tools people have and the coordination they need. Coming from the flagship CSCW conference, it also models how collaboration research is framed and reported.
A CSCW conference talk on collaborative immersive analytics — groups working together with data inside immersive or shared visual environments. The title's question, "Is It My Turn?", signals a focus on turn-taking and coordination, and the talk distinguishes teamwork (how members coordinate with each other) from taskwork (the analytic work itself). Look for how the researchers measured or assessed these two dimensions.
It connects CSCW directly to advanced interface paradigms and display technologies, showing how immersive presentation changes group coordination rather than simply making data prettier. The teamwork/taskwork distinction is a useful analytical lens you can carry into your own group-system designs.
A SIGCHI presentation proposing a future HCI research agenda for emerging technologies in K-12 education, from Van Mechelen, Smith, and colleagues. It likely surveys technologies entering classrooms — which may include AI, data-driven tools, and new interaction modalities — and identifies open questions and design challenges for researchers and practitioners. The framing is agenda-setting rather than reporting a single study.
It covers the emerging trends and future directions subtopic explicitly, and it grounds abstract speculation in a concrete setting with real stakeholders, oversight concerns, and constraints. It also shows students what a research agenda looks like, which is useful if you are choosing a project direction.
These videos extend the module's core outcomes into multi-user and advanced contexts. Discussions of workflows and role handoffs in the data science talk give you a realistic target for Hierarchical Task Analysis (MO3), since a collaborative goal must be decomposed across people as well as steps. The immersive analytics talk makes mental models and memory constraints concrete (MO1, MO2): when several collaborators hold different models of a shared space, and when immersive displays load attention and working memory, systematic coordination errors follow. All three presentations also model evaluation practice (MO4) by showing how researchers study real users in real settings and report findings to a professional audience, and the design implications and open questions they raise are the kind of input that should shape a low-to-mid fidelity prototype (MO5) before it is built.
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.