SunyoungKim,PhD Quiz #1 Average: 13 Max: 19 Min: 7 6 4 4 4 4 - - PowerPoint PPT Presentation

Human-Computer Interaction

• 9. Understanding Users – Part 4. Data analysis

SunyoungKim,PhD

Quiz #1

Average: 13 Max: 19 Min: 7

2 2 4 6 3 3 4 4 2 4 2 1 19 18 17 16 15 14 13 12 11 10 9 8 7

• 1. What did Duglas Engelbart invent?
• The first mouse

• 2. Provide two definitions of Human-

Computer Interaction.

• The study of how humans interact with computer systems: a broad

term that covers all aspects of the ways in which people interact with computing systems

• The design of a user interface (UI) to allow a user to interact with

computer systems with minimum effort and frustration

• 3. You are trying to convince your
• rganization to use a UCD approach of

HCI for software development. Provide two reasons to use HCI.

• You can evaluate what a user desires prior to your design
• Allow designers to see what users want /need
• Allows designers to understand how they would interact with certain

systems

• 4. What does “one size does not fit all”

mean? And, how does this concept influence your analysis of the users and their tasks?

• One solution cannot satisfy all users
• You need to determine who your target users are and focus on

them in the analysis & design

• 5. Why does the number of ideas under

consideration reduce as we iterate through the design cycle?

• Through iterations, designers get to realize which ideas are more

feasible to accomplish a goal

• 6. Why is it difficult to identify user

needs by explicitly asking users about their needs?

• Users often don’t know what’s possible
• Users often can’t articulate clearly what they need to achieve their goals

• 7. What is a definition of “Task

Analysis”?

• The process of examining the way in which people perform their tasks
• Verify that the set of actions employed by the user does accomplish the

task.

• Explicitly describe the procedure that the user actually employs since

this may be different from the expected series of actions.

• 10. List two types of interviews, and describe

two pros and cons of each method.

• Structured
• Semi-structured

• 11. What kinds of questions do you ask in

interviews and what kinds in surveys? Give the kinds with an example question per method.

• Interview: Qualitative, Open-ended, Exploratory, Descriptive,

Experiential

• E.g., “What did you think when you experienced a very slow

loading time to access a website?“

• Survey: Quantitative, Close-ended
• E.g., “Which of the following best describe your reaction to a very

slow loading time to access a website?”

(1) Extremely unpleasant (2) Very unpleasant (3) a bit unpleasant (3) I didn’t care

• 12. “How angry do you usually feel when your

attempt to submit an assignment through Canvas fails?” Describe the problems (at least two) with this interview question, and revise the question to

• vercome the described problems.
• Problem
• A leading question, Presumption
• Asked about a general idea instead of a specific past event
• A revised question:
• “Have you ever experienced any issues when submitting an

assignment to Canvas?” With a follow-up question “How did you feel?”

• 13. What is the name of this type of

question?

Likert Scale

Last week

• Human models

“The models people have of themselves, others, the environment and the things with which they interact. People form mental models through experience, training and instruction”

• An explanation of someone's thought process about how something

works in the real world

• Enable people to reason about a system
• Affect the way we see and interpret reality
• When users approach an unfamiliar system, they subconsciously refer

to their mental model, User’s model “To break a mental model is harder than splitting the atom.”

• -Albert Einstein

Recap: Mental models

Recap: The Model Human Processor

• Published by Card, Moran & Newell, 1983, in the book “The

Psychology of Human-Computer Interaction”

• Consider humans as information processing systems
• Core cognitive aspects
• Attention
• Perception and recognition
• Memory
• Reading, speaking, and listening
• Problem-solving, planning, reasoning and decision-making, learning
• Describes how a user interacts with a computer system

Processors:

• Perceptual
• Cognitive
• Motor

Memory:

• Working memory
• Long-term memory

Recap: The problem with the classic ‘7±2’

• George Miller’s theory of how much information people can remember
• People’s immediate memory capacity is very limited
• The number of objects an average human can hold in working memory

(short-term) is 7 ±2

• Example:

DECIBMGMC vs. DEC IBM GMC 6174591765 vs. 617‐459‐1765

Recap: Chunking

Recap: Recognition over recall

• Recall – information reproduced from memory
• Recognition

– Presentation of information provides cues that information has been encountered before – Easier because of cues (context) for retrieval

Recap: Fitts’s law

• Fitts’s law predict the amount of time taken to move to and select a

target

• It is faster to hit larger targets close to you than smaller targets

further from you

• T (Travel time to hit the target) increases as the distance to the

target (A) increases

• T decreases as the size of the target (W) increases

Today’s agenda

• Affinity diagram
• Persona

Affinity diagram

Affinity diagram

“a UX designer’s tool, used to capture and synthesize qualitative data” The affinity diagram brings together issues and insights across all users into a hierarchical diagram to reveal the scope of the problem.

• Gathers large amounts of language data (ideas, opinions, issues) and
• rganizes them into groupings based on their natural relationships
• A single picture of the customer population a design will address
• Shows how the work hangs together rather than breaking it up in lists

and what matters

• Used to brainstorm and generate ideas

Affinity Diagram

• Affinity Diagrams are generated during group session
• Each observation/idea/note is copied to a post-it
• Notes are hierarchically organized into themes, based on the focus of

the project

• Usually done in a chaotic fashion, with design team members running

back and forth with post-its and yelling ideas to each other

Steps: Creating an Affinity Diagram

Collect Data Extract raw data Synthesize data Understand

1.Collect Data

• Interviews
• Surveys
• Observations
• Field notes
• …

• 2. Extract raw data
• Record all of your interviews and re-listen to them as you

transfer the insights on to post-it notes

• Start each post-it note from the perspective of the user
• For each person that you spoke with, choose a different color

post-it note

• 3. Synthesis

Arrange them into clusters that make sense to you! In another word, sort the sticky notes into groups, such as (but not limited to):

• Biographical information
• Frustrations
• Interactions/touch points
• goals/motivations

• 3. Synthesis
• Start by looking for two ideas that seem related in some way. Place them

together in a column off to one side

• Look for ideas that are related to those you've already set aside and add

them to that group

• Look for other ideas that are related to each other and establish new

groups

• 4. Create header cards

Header: An idea that captures the essential link among the ideas contained in a group of cards

• Finding already existing cards within the groups that will serve well as

headers and placing them at the top of the group of related cards

• Alternatively, discussing and agreeing on the wording of cards created

specifically to be headers

• Discovering a relationship among two or more groups and arranging

them in columns under a super-header. The same rules apply for super-headers as for regular header cards

• It should be concise and have a clear meaning!

• 5. Draw finished diagram
• Write a problem statement at the top of the diagram
• Place header and super-header cards above the groups of ideas
• Review and clarify the ideas and groupings
• Document the finished Affinity Diagram

• 5. Draw finished diagram

Issues in implementing continuous process improvement

Survey report

• 1. Analysis of Quantitative (Survey) Data

Do you want to report…

• how many people answered a, b, c, d?
• the average number or score?
• a change in score between two points in time?
• how people compared?
• how many people reached a certain level?

Common descriptive statistics

• Count (freq)
• Percentage
• Mean
• Mode
• Median
• Range
• Standard dev
• Variance
• Ranking
• a. How many people answered a, b, c, d?
• b. The average # or score?
• c. A change in score between two points in

time?

• d. How people compared?
• e. How many people reached a certain level?

Dig bigger!

• Did different groups show different results?
• Were there findings that surprised you?
• Are there things you don’t understand very well – further

study needed?

• 2. Interpret & Visualize data

Visual representations (Graphs)

Report

Reflect

• What did we learn?
• What conclusions can we draw?
• What are our recommendations?
• What are the limitations of our analysis?

Report

• Be explicit about your limitations
• Know the claims you cannot make
• Do not claim causation without a true experimental design
• Do not generalize to the population without random sample and

quality administration (e.g., <60% response rate on a survey)

Represent the users

• 1. User profile

User Characteristics User Characteristics ATM Customer characteristics TM Customer characteristics Age Range from about 12 to 80+ Sex Both male and female Physical limitations May be fully able-bodied or may have some physical limitations in relation to hearing, sight, mobility, use of hands, or wheelchair use. Will be of varying heights Educational background May have only minimal education qualifications and possess limited literacy and numeracy skills Computer/IT use May have little or no prior experience of computer or IT use Motivation May be very motivated to use the ATM, particularly if they can do their banking quickly and avoid waiting in long lines at the bank Attitude Attitudes to use may vary, depending on the services the ATM offers the reliability of the technology itself, and the attitude of users toward computers

• 1. User profile

• 1. User profile

User Characteristics User Characteristics ATM Customer characteristics TM Customer characteristics Age 12-25 25-50 50-80+ Sex Both male and female Both male and female Both male and female Physical limitations Educational background Computer/IT use Probably have some prior experience of computer or IT use May have little or no prior experience of computer or IT use May have little or no prior experience of computer or IT use Motivation Attitude

• 2. Personas

A precise description of a hypothetical user and what they suffer from and wish to do when using a system

• Based on demographics and on patterns of behavior
• Not real; an imaginary example of the real users
• Make it as specific as possible: give a name, personality, etc.
• A concrete person in the designer’s mind
• A shared basis for communication
• At least one primary persona – the main focus of the design

Essential question in developing a persona

• What are the classes of users?
• What do they do? And Why?

Why personas?

• It’s hard to reason about users in aggregate, and impossible to please

everyone

• General users have too many conflicting goals
• It’s easier to reason about specific fictional people
• Specific personas have clear, well-articulated goals

Defining personas

• Personas represent a class or type of user but should be used as an

individual

• Not a stereotype – which are usually based on assumptions not factual

data

• Personas must have motivations / goals
• Identify major clusters from multiple user interviews/inquiries
• Give them names

Group project

Group project: Data collection & analysis

Collect interview data from 4 potential users (other than yourself or team members!) and collect survey data from 10+ potential users. Submit the collected data. Submit: 1. Interview transcripts 2. Survey data

#Disclaimer. Further instruction of this submission can be given verbally during class or through Piazza.

Group project: Data collection & analysis

Format

• Add a team name and a description of your project proposal on top
• Link to your project webpage on top
• Should be accessible
• Should have four pages with full contents (no link to a pdf file):

§ Team intro § Project Proposal § Review of existing systems § Data collection plan: Interview/survey

• a PDF file, 12 point scale in Times New Roman, 1.5 line spacing
• Include: Who contributed to the original writing of each section and who helped

improve and/or review at the end

#Disclaimer. Further instruction of this submission can be given verbally during class or through Piazza.

Group project: Data collection & analysis

Rubric

• Interview scripts (4pt)
• -1pt per missing one interview (4 in total)
• You will lose significant points if there are not enough impromptu "follow-up"

questions to follow up with an interviewee's answer (e.g., what/why/how questions)

• Survey data (1pt)
• You will lose 50% if your submission does not follow the format
• You will lose 20% if it’s a late submission (Not accepting a late submission after 24 hrs)

Due by Midnight 10/12

#Disclaimer. Further instruction of this submission can be given verbally during class or through Piazza.

By next class

q Submit an individual project P2. Part1: Data collection design by 10/9 midnight