Design Thinking Synthesize and combine new ideas to create the - - PowerPoint PPT Presentation

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Design Thinking Synthesize and combine new ideas to create the - - PowerPoint PPT Presentation

Design Thinking Synthesize and combine new ideas to create the design SWEN-444 Selected material from The UX Book , Hartson & Pyla HCI Design Three design paradigms (patterns of thinking) Engineering focus on design for HCI


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Design Thinking

Synthesize and combine new ideas to create the design

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Selected material from The UX Book, Hartson & Pyla

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“HCI Design”

  • Three design paradigms (patterns of thinking)
  • Engineering – focus on design for HCI usability performance

achieved through evaluation and iteration

  • Human Information Processing (HIP) – cognitive science based

design model of human mind as information processor

  • Design-Thinking – consider emotional and phenomenological,

social and cultural aspects for the UX

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Example – Car Design

  • Engineering view – features (cruise control),

performance (fuel economy), human factors (airbags)

  • HIP view – optimal use of human senses (position of

instruments, use of no look tactile cues)

  • Design thinking view – cool factor, joy of driving, life style

considerations, pride of ownership

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Phenomenology

  • Philosophical study of experience and consciousness

about things that happen and can be observed (phenomenon)

  • Not logical deduction or reflection …
  • rather individual intuitive understanding
  • For HCI – the emotional impact of an interactive

relationship with a product as part of our lives

  • Technology ubiquity shifts the UX emphasis from

efficiency to presence

  • Meaning in life
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iPhone Android

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Design Thinking

  • Creative and innovative UX design concept first
  • Combination of art, craft, science, invention
  • Followed by functional and interactive design
  • Long term emotional impact
  • Aesthetics
  • Social and value oriented interaction
  • About situated interaction – the importance of place
  • How technology takes on “presence” in user’s life
  • May be market driven (think Apple)
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SLIDE 8

Design Perspectives

  • Filters to guide design thinking
  • Ecological perspective
  • How the system interacts with its external environment
  • How work gets done in context of system infrastructure
  • Interaction perspective
  • How users operate the system – task orientation
  • Emotional perspective
  • Emotional impact
  • Social, cultural implications
  • Aesthetics and joy of use
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Plutchik's Wheel of Emotions

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Basic Emotions

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Personas - a Pretend User

  • A specific (but imaginary) person in a specific work role;

a personification

  • Make design thinking more concrete
  • User roles are too broad – can’t satisfy everyone
  • Focus and satisfy one “person”
  • Minimize designer bias to design for their own needs;

engage designer empathy

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Personas – a Pretend User

  • Represent a class of users
  • Composite user archetypes based on behavioral data gathered

from many actual users

  • Aggregate patterns of behavior
  • Specific work or lifestyle related roles
  • Using personas in design …
  • Select a small number of personas from the user class
  • Pick one as primary and design for that one
  • Adjust as necessary to accommodate the others
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Persona Characteristics

  • Domain and system specific – not reusable
  • Characterize ranges of behavior, not averages
  • A cast of personas representing different clusters of behaviors
  • Personas have goals
  • They engage the empathy of stakeholders and designers toward the

target user

  • Make decisions based on the cognitive and emotional dimensions of the

persona

  • Note the power of fictional characters in television programs, movies,

and novels

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Personas are Based on Research

  • Personas are typically based on synthesizing the

qualitative results of ethnographic interviews and

  • bservations
  • Market segmentation models and research data

(personas are a common marketing research tool)

  • Plus other sources of elicited information – interviews,

surveys, literature research

  • Requires both detailed analysis and creative synthesis
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Constructing Personas

  • Establish a persona hypothesis
  • Segment use across a set of observed behavioral variables (also

called axes or ranges)

  • E.g., computer literacy, annual income
  • Identify significant behavior patterns
  • Clusters of users with shared behavior across multiple behavioral variables (6-8)
  • Valid patterns demonstrate logical or causative relationships between clustered

behaviors

  • Combine one of more patterns into a persona role
  • Synthesize persona characteristics and relevant goals
  • Review for completeness and distinctiveness
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Map Interview Subjects to Behavioral Variables

  • Map users against each variable range that

applies

  • Relative placement is more important than precision
  • Looking for clustering of multiple subjects on each variable axis

[Cooper and Reimann]

15-30 Variables per role is typical Service-oriented Price-oriented

User 3 User 2 User 1,4,5

Necessity Only Entertainment

User 1,4 User 5 User 3 User 2

Patterns:

  • User 1 & 4
  • User 3

Let’s Go Shopping! Behavioral variable

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Synthesize Characteristics

  • Give each major pattern a brief description, such as "the bargain-

hunter" or "the impulse-buyer"

  • Synthesize details from the data
  • Describe use environment, typical workday (or other relevant time period), current

solutions and frustrations, relevant relationships, etc.

  • Stick to observed behaviors
  • Avoid too much fictional, idiosyncratic biography
  • A persona is a design tool, not a character sketch for a novel
  • Carefully select a first [and last] name for the persona
  • Evocative of the type of person the persona is
  • Add some demographic information: age, geographic location,

relative income (if appropriate), job title

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Example Persona

  • Digital Camera users:
  • Alice “Conservative Sharer”
  • Female, married, children, ages 25-54
  • Takes 250-500 snapshot photos a year
  • Image quality is not a high motivator
  • Subjects include family, schools events, and vacations
  • Computer literate
  • Occasional user of social networking sites for photo sharing
  • Prints 10% of all photos
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Ideation

  • Collaborative group process for forming conceptual

design ideas; i.e., “applied design thinking”

  • Idea creation
  • Idea critiquing – review and judgment
  • Brainstorming
  • Team activity
  • Stream-of-consciousness
  • Generate as many ideas as possible
  • Don’t be critical of or constrain creativity
  • Brainstorming sessions generate a lot of material that

must be filtered and organized

  • Categorize, sort, vote
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Dissent

  • An alternative to brainstorming
  • Participants encouraged to criticize ideas
  • Criticism surfaces problems that forces new thinking to

respond

  • Produces more productive and innovative ideas

BLACK BOX THINKING: Why Most People Never Learn From Their Mistakes—But Some Do by Matthew Syed

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Semantic Networks

  • Represent information the way your brain stores it:
  • Concepts
  • Associations
  • Visually organize your thoughts through a combination
  • f drawing and text
  • Sketch diagrams to replace words and trigger easier

memory recall

  • Useful for grouping related items, building menu structures
  • Use for note taking, planning, summarizing, exploring

ideas

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Examples of Semantic Networks

  • Mind maps
  • Words, ideas, tasks, or other items linked to and arranged around a

central key word or idea

  • Used to generate, visualize, structure, and classify ideas
  • See http://www.mindjet.com
  • Fishbone diagrams
  • Cause and effect diagrams
  • Categorize causes contributing to an effect or problem
  • Primary and secondary causes
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Mind Map Example

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Fishbone Diagram

Categories

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Sketching

  • Rapid creation of freehand drawings
  • Expressing preliminary design ideas
  • Focusing on concepts rather than details
  • Reinforces design thinking, augments communication
  • Explore and expand design ideas
  • Sketches are not prototypes
  • They are abstract, incomplete, not artistic, disposable,

fast, annotated

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“Overfitting”

  • In computer science, pursuing complex models not

supported by detailed data

  • By analogy, in UX design, to start keep it simple, avoid

complexity

  • Don’t over design in the beginning, iterate