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Course Intermediate

User Experience and Behavior Design

By the end of this course

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01

Research and Definition

When observation, interviews, and surveys are each chosen; deriving personas, task lists, and journey maps from data; reading existing solutions and writing the problem before the solution.

  1. 01 User Research Methods What question observation, interviews, and surveys each answer; measuring interview saturation, the limit of inferring prevalence from a small sample, and the ethical framework of research.
  2. 02 User Profiles That a persona is not a fabricated person but a behavior set derived from data; clustering participants, calculating the explanatory power of role-based grouping, and the ethical limit of a persona.
  3. 03 User Stories and Tasks Turning a need into a story and a story into a task; checking for solution bias in a story, and ranking the task inventory by frequency, failure rate, and failed attempts.
  4. 04 Journey Maps Breaking the end-to-end experience into stages; calculating channel share, drop-off rate, and wasted person-minutes; and why the longest stage and the most-abandoned stage are not the same.
  5. 05 Competitive and Current-State Analysis Reading existing solutions decision by decision; spotting a convention through its adoption rate, measuring the cost of a deviation in step count, and the limits observed during the review.
  6. 06 Problem Statement Merging five lessons' findings into a single table; ranking by evidence strength and lost time, the parts of a problem statement, and preventing a solution from seeping into the statement.

02

Information Architecture and Flow

From content inventory to grouping, from navigation models to flow diagrams; wireframe and prototype fidelity levels, usability heuristics, and finding problems with a small sample.

  1. 01 Content Inventory and Grouping Deriving structure from content; the inventory's columns, building a similarity matrix from card-sorting data, threshold-based grouping, and detecting cards without consensus.
  2. 02 Navigation Models Comparing hierarchical, flat, and matrix navigation; computing the trade-off between menu width and depth, and how reorientation cost shifts the best width.
  3. 03 Flow Diagrams Building the flow as a graph; searching by computation for unreachable states and dead-end nodes, distinguishing whether a cycle is a flaw, and how path count determines testing load.
  4. 04 Wireframe and Prototype Fidelity's three independent axes — visual, content, and interaction; separating a prototype's node coverage from its task coverage, and how the fidelity level affects preparation cost.
  5. 05 Usability Heuristics Applying an established set of evaluation criteria principle by principle; computing how much multiple evaluators' findings overlap, and why a single evaluator misses the most severe problem.
  6. 06 Usability Testing Finding problems with a small sample; computing the discovery curve, why the five-participant claim collapses on a non-uniform problem set, and the uncertainty of duration measurement in a small sample.

03

Behavior Design

Intuitive and deliberative decision-making, cognitive load, the motivation–ability–trigger framework, the habit loop, the effect of defaults, and recognizing dark patterns.

  1. 01 Dual-Process Thinking Whether a decision is made by the fast intuitive system or the slow deliberative system, how salience in a result list determines the choice, and computing the time cost of switching to the deliberative system.
  2. 02 Cognitive Load Computing the effect of the number of options on decision time with the Hick–Hyman relation, grouping's counterintuitive result, counting independent decision points on a screen, and the effect of decision fatigue on error rate.
  3. 03 Behavior Models That motivation, ability, and a trigger must hold at the same time for a behavior to occur; computing the effect of two types of intervention on a population; and why the motivation gain fades.
  4. 04 The Habit Loop How the loop formed by cue, routine, and reward strengthens with repetition, the cost of weakening one of the three parts, and why habit strength is not the same thing as the benefit delivered to the user.
  5. 05 The Power of Defaults Computing choice architecture's effect on participation rate across two settings, defining regret rate as a legitimacy criterion, and how switch cost turns a default into a trap.
  6. 06 Social Proof and Authority How visible counts produce an information cascade that suppresses independent judgment, how a popularity list feeds itself, and that an authority signal carries value only when it is more reliable than the user's own knowledge.
  7. 07 Progress and Feedback Computing whether a progress indicator correctly reports the work remaining, distinguishing deception that raises the completion rate, and tying feedback delay to indicator classes.
  8. 08 Dark Patterns and Ethics Turning a design decision's legitimacy criterion into countable indicators, and recognizing and rejecting the patterns of urgency, hidden costs, the roach motel, the consent trap, and false scarcity.

04

Measurement

Producing a composite metric from task success, duration, and error rate; locating drop-off points in the conversion funnel; designing A/B and multivariate tests; and combining quantitative findings with qualitative observation.

  1. 01 Experience Metrics Reporting task success together with its confidence interval, how duration-distribution skew misleads the arithmetic mean, and producing a weighted composite metric from three metrics.
  2. 02 Conversion Funnel Step-by-step drop-off analysis of the borrow flow, how the highest drop-off rate and the largest user loss occur at different steps, and how an aggregated funnel hides differences between segments.
  3. 03 A/B and Multivariate Testing How sample size relates to the detectable difference, the confidence interval of a rate difference, how early stopping inflates the false-positive rate, and the multiple-comparison correction in a multivariate test.
  4. 04 Combining Quantitative and Qualitative Data Combining the two sources along the funnel-step axis, narrowing the question that arises when the rankings diverge, reporting coverage, and reading a qualitative rate with its confidence interval.

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