When a usability test session ends, you are left with a pile of observations: a participant clicked the wrong button, backtracked three screens, typed a date in the wrong format, or quietly gave up. Error analysis is the discipline of turning those raw observations into a diagnosis. Instead of reporting "the user got confused," you classify what kind of error occurred (a slip, a mistake, a mode error, a data-entry error), trace it back to a root cause, and name the specific interface element — a label, an affordance, a layout, a feedback message — that produced it.
This matters in CEN3722 because redesign decisions are only as good as the diagnosis behind them. Two errors that look identical on a screen recording can have completely different causes: one may be a momentary slip by an otherwise capable user, while the other reveals a fundamental mismatch between the user's mental model and the system's conceptual model. Those two findings call for very different fixes. You also need a defensible way to separate critical errors (data loss, task failure, security or safety consequences) from minor ones (a brief hesitation, an easily recovered misclick), because you will never have time to fix everything.
The videos below approach this from three angles: the metrics you use to quantify errors and task performance, the research methods that generate error data in the first place, and a research-level look at how touch-based errors are captured and investigated on real devices. As you watch, keep asking three questions: How would I count and record this kind of error? What would I need to observe to infer its cause rather than just its symptom? And how would I decide whether this error is worth a redesign cycle?
This video from the Eleken design agency walks through six commonly used UX metrics and explains what each one measures and when to apply it. Based on its title and description, expect coverage of performance and satisfaction measures such as task success or error rates, time on task, and standardized satisfaction scores, framed around the problem of knowing whether a product actually works for real users rather than just for the team that built it.
Error analysis depends on quantification — you cannot judge severity or frequency without metrics — and this video gives you the vocabulary for counting errors and task failures in a consistent, reportable way. It supports the subtopic of distinguishing critical from minor errors by showing how numbers are used to argue that an issue matters.
Kerev Design contrasts user testing with usability testing, two methods that are frequently confused in practice. The video situates both within the broader UX research process and clarifies what question each method is designed to answer — roughly, whether people want the product versus whether people can actually operate it.
Correctly interpreting an error requires knowing what the study was designed to reveal, since an error observed in a usability test points at the interface while a problem surfaced in user testing may point at the concept itself. This distinction directly supports root cause analysis and keeps students from attributing the wrong cause to an observed failure.
This short conference presentation from ACM SIGCHI describes a new data collection approach for investigating touch-based errors, using a smartphone system as its case study. As a research talk it is likely to cover how touch interaction data is instrumented and captured, what kinds of touch errors the method distinguishes, and what the collected data reveals about where and why mistouches occur.
It is a peer-reviewed research source from the premier HCI professional organization, and it demonstrates error analysis at the most granular level — mapping specific errors to specific interface elements and target regions on a screen. The case-study format shows students what rigorous, evidence-based error classification looks like beyond informal note-taking.
Together these videos move students from observation to diagnosis to prioritization. The metrics video supplies the measurement vocabulary needed to spot recurring patterns and quantify frequency and severity, supporting MO1 and MO2. The user testing versus usability testing comparison helps students match findings to the right level of cause — concept, task flow, or control — which is essential for the accurate pain-point identification required by MO1 and for writing a defensible rationale behind each proposed change under MO3. The SIGCHI case study models fine-grained mapping of errors to specific interface elements, giving students a concrete example of the evidence trail that justifies a targeted redesign rather than a guess, and reinforcing the severity judgments called for in MO2.
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.