Hci02 HumanFactor Users

8/19/2019 Hci02 HumanFactor Users

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Master on Software Engineering :: Human-Computer Interaction

Dr. Sabin-Corneliu Buraga – profs.info.uaic.ro/~busaco/

Human

-

Computer Interaction

the human factor

http://usabilitygeek.com/an-introduction-to-website-usability-testing/


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“The world is divided into people who think that they’re right.”

Diedre McGrath


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Reality

Multiple interaction idioms

Jenifer Tidwell, 2006


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Reality


many familiar types/styles of user interfaces


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Reality



command-line languagesinstructions, keywords, abbreviations, special symbols,I/O mechanisms


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Reality

a classical UNIX console – see @climagic on Twitter


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Reality



interactive forms + spreadsheets


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Reality



menus


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Reality



direct manipulation + hypertext

f


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S f i i C i


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Reality



natural interaction

typical examples: gestures, natural language (e.g., speech)

M t S ft E i i H C t I t ti


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Reality



natural interaction

conversational UI (CUI)



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Dr. Sabin-Corneliu Buraga – profs.info.uaic.ro/~busaco/examples: interconnected.org/home/2015/06/16/conversational_uis

apps as chats



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Reality



each has its own vocabulary of items, actions,sensorial – e.g., visual, auditory – representations

Master on Software Engineering :: Human Computer Interaction


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Reality



each has its own vocabulary of items, actions,sensorial – e.g., visual, auditory – representations

user experience (UX)



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Reality


formstext editors

graphic editorsspreadsheets

Web browserscalendars



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Reality


multimedia playerscharts

immersive games Web pages

social (virtual) environmentse-commerce/e-business applications



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Reality

Heterogeneity of existing platforms

Jean Vanderdonckt, 2006



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Reality

Heterogeneity of existing platforms

desktop (personal) computersmobile devices: smartphone, tablet, automobile

output means: printer, fax, advertising screen etc.home appliances – e.g., (smart) TV, washing machine,…

wearable devices: watch, camera etc.social-oriented devices: ATM, info kiosk, photo booth

entertainment platforms – e.g., Blu-ray player, Wii, XBox



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Reality

Variety of performed tasks




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Reality


Nokia findings in the mobile context (2007):blogging, browsing, chatting, downloading, e-banking,

instant messaging, playing games,searching: locations, people,…



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Reality


domain-oriented tasks:business, programming, research, studying, training etc.



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Reality

Multiple contexts




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Reality

Multiple contexts

at work

examples: business & office applications, IDEs, tools, etc.



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g g p


Reality

Multiple contexts

at home

examples: entertainment & leisure softwaremultimedia players, social networks, games,…



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g g p


Reality

Multiple contexts

during travelling

examples: PIMs, planners, geo-related services,instant messaging software, recommenders,…



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g g p


Reality

Multiple contexts

specific locationsconcert places, hospitals, malls, restaurants,…

examples: “smart” apps/services provided by mobile and/or wearable devices/appliances


di i


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In which context, tablet computers

(and related devices) could be the best choice?

case study: iPad – http://pttrns.com/ipad

discussion



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Reality

User volume and diversity




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Reality

User volume and diversity

traditional usersa community of interest vs. population of a country

+special usershaving certain disabilities and/or needs



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How about users?

http://buffalobillgates.tumblr.com/



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Knowing users…

Who is the user?



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Knowing users…

Probably, we do not like her/him!



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Knowing users…

We must discuss with her/him



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Knowing users…

We need to observe her/him

e.g., RUM (real user monitoring)

a case-study: http://community.hpe.com/t5/Business-Service-Management/Deep-Diving-End-User-

Experience/ba-p/6833418



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Knowing users…

The user is the person who…

directly interact to the (software) product/service



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Knowing users…


involve others to use the product/service



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Knowing users…


obtain output from a given product/service



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Knowing users…


is making decisions regarding the acquisitionof a product/service



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Knowing users…


is using the product/serviceprovided by our competitor



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Knowing users…

User types (Eason, 1987)

primary

directly involved in the use of the product(frequent hands-on beneficiary)



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Knowing users…


secondary

occasionally and/or indirectly uses the product/service



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Knowing users…


tertiary

affected by a product/service presentationor influenced by a certain product/service



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Knowing users…

Identifying characteristics of the user population

demographics

age, gender, ethnicity +psychological profile



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Knowing users…


education

elementary school vs. doctoral studies



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Knowing users…


physical abilities

senses & perception – e.g., visual acuity (short/long-term) memoryanatomic features: length of arms, height, muscles,…



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Knowing users…


computer-related abilities

general knowledge regarding the use of computersor the target platform – e.g., a specific game console



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Knowing users…


computer-related abilities

aspect of interest:technophobic persons



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Knowing users…


skills

readingusing devices – keyboard, mouse, remote control,…interpreting the output

etc.



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Knowing users…


experience related to the application domain



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Users without prior knowledge aboutsemantic Web can understand

and operate this user interface?



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Knowing users…


experience regardingthe type of (software) product/service



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Knowing users…


working environment+

other social factors



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Knowing users…


relations with other persons+

the use of communication patterns (1:1, 1:n, m:n)


discussion


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Web application providing online communicationservices used in the context of winter Olympic games


discussion


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user population

main user categories: athletes, their friends & families,administration personnel, audience, sport reporters,…


discussion


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interaction

types of devices: desktop PCs, mobile phones,info kiosks, public screens,

Web-friendly devices (smart phones, tablets),…


discussion


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conveying information

multiple formatscharts

rich versus limited data/interaction

search possibilitiesfiltering

reusing – e.g., sharing data to other apps/services

etc.



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Knowing users…


techniques:user surveysinterviews

(in)direct observations: user tracking, feedbackreal user testing



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Knowing users…


techniques:user surveysinterviews

(in)direct observations: user tracking, feedbackreal user testing

user research



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Knowing users…

http://www.nngroup.com/articles/which-ux-research-methods/



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Knowing users…

Remark:

a software application could havemultiple audiences



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Knowing users…

Remark (Rogers, 2007):

there are few cases when users really know what they want



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Knowing users…

Remark (Rogers, 2007):

“natural” tasks must be anticipated by softwareon the basis of the perceived users’ behavior


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Knowing users…

Laws of human-computer interaction(Jeff Raskin, 2000)

a computer shall not harm your work or,

through inaction, allow your workto come to harm



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Dr. Sabin-Corneliu Buraga – profs.info.uaic.ro/~busaco/www.flickr.com/groups/insults/



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Knowing users…

Laws of human-computer interaction(Jeff Raskin, 2000)

a computer shall not waste your time or

require you to do more workthan is strictly necessary



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Knowing users…

User Interface

Humane Interface

(Raskin, 2000)

an interface is humane if it is responsive tohuman needs and considerate of human frailties



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Knowing users…

User Interface

Humane Interface

(Raskin, 2000)

an interface is humane if it is responsive tohuman needs and considerate of human frailties

existing UIs are too complex for the majority of users



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Utilizatorii

thanks to our graduate Alexandru Rugină



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Dr. Sabin-Corneliu Buraga – profs.info.uaic.ro/~busaco/Can we model users? Master on Software Engineering :: Human-Computer Interaction


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Cognition

Cognition is a knowledge acquisition process

to understand the external world via sensation, perception, and reasoning

Wang, 2008



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Cognition



Wang, 2008



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Cognition



Wang, 2008



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Cognition

Cognitive engineering is a software engineering

principle that states the cognitive complexity andhuman intelligent manageability should beaddressed as the dominant problem in almost

all processes of software design, implementation,and maintenance

Wang, 2008



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Cognition

Cognitive informatics

Wang, 2002



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Cognition

Important aspects:

knowledgebehaviorsexperience

skills



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Cognition

Important aspects:

knowledgebehaviorsexperience

skills

modeling the user



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Cognition

Cognitive abilities define, in an abstract manner,

the model of a user



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Utilizatorii



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Cognition

5 major senses:

sight – ophthalmoceptionhearing – audioceptiontouch – tactioceptiontaste – gustaoceptionsmell – olfacception

www.tutis.ca/Senses/



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Cognition

Non-traditional senses:

balance – equilibrioceptiontemperature – thermoception

kinesthetic sense – proprioceptionpain – nociception

time – chronoception



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Cognition

Non-human senses(not analogous to human senses):

echolocationelectroreception

magnetoception



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Cognition

Card, Moran & Newell, 1983; Wickens, 1984



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Cognition

Memory

short-term (working) memory +

long-term memory



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Cognition

Memory

data codification

types of stored things, associations



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Cognition

Memory

capacity

number of remembered things



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Cognition

Memory

duration (decay time)

how long a thing persists into memory


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the need of visual representation


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http://www.interaction-design.org/encyclopedia/visual_representation.html

RDFSpecies by C. Serban & D. A. Popovici (2012)



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Cognition

Memory

audio data

codification = sounds

capacity : ~5decay : ~1500 ms



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Cognition

Memory

audio data is pre-intentional

“What you’re saying?...”



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Cognition: perception

Bottom-up perception

uses stimuli



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Top-down perception

based on contextspatial and/or temporal data

influence over long-term memory



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Chunk = perceptual/cognitive unit



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Chunk division – e.g., grouping – depends on

the user background knowledge andhow data is presented



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Chunk division – e.g., grouping – depends on

the user background knowledge andhow data is presented

remember IA?



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Common technique:

TLA (Three-Letter Acronyms)



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Chunking – example:

B M W R C A A O L I B M F B I

MWR CAA OLI BMF BIB

BMW RCA AOL IBM FBI



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Common technique:

the codification of unrelated digitsuses 2—4 chunks



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Users memorize phone numbers by grouping:

0232201090

0232 20 10 90



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Working memory:

capacity: 7 ± 2 chunks –

George Miller

decay: 7 sec. (5—226 sec.)repetitions



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Working memory:

capacity: 7 ± 2 chunks –

George Miller

decay: 7 sec. (5—226 sec.)repetitions

influenced by interferences



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anterograde amnesia – inability to store new memories

Memento (2000):http://www.imdb.com/title/tt0209144/



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Long-time memory:

elaborate repetitions move chunksfrom working memory to long-time memory

via connections (associations) with other chunks



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Long-time memory:

elaborate repetitions move chunksfrom working memory to long-time memory

via connections (associations) with other chunks

influenced by context



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Recognition versus recall

representations must easily beperceived and recognized

metaphors + idioms


discussion


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from icons & symbols to visual metaphors


discussion


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discussion


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discussion


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visual metaphors and/or idioms?


discussion


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“ Metaphors create insight. But they also distort.They have strengths. But they also have limitations.”

Gareth Morgan

http://www.orangecone.com/archives/2010/05/smart_things_ch_7.html


discussion


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the use of metaphors and idiomsin the context of wearable devices


C i i i


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humans remember visual properties of things

e.g., color, shape,…


C i i i


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humans have difficulties in memorizing/recallingarbitrary data

e.g., phone numbers, software serial numbers


C i i i


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UIs must facilitate recognition


C i i i


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users tend to annotate things

using external means to modify existing (mental, internal) representations


C iti ti


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users tend to annotate things

using external means to modify existing (mental, internal) representations

examples:underlining words, taking written/electronic notes,…


C iti ti


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cognitive tracing

implies external manipulation of thingsin order to (re)structure or (re)arrange them


C iti ti


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cognitive tracing

implies external manipulation of thingsin order to (re)structure or (re)arrange them

examples:scrabble, bookmarking, tagging, etc.


C iti ti

discussion


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C iti ti


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UIs must offer external representations of the concepts used by the software

decreasing the cognitive overload


GraphicResources

Correspondence Design Uses

ShapeMark position


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Marks

p

Orientation

Size

Texture

Saturation

Color

Line

Literal

(visual imitation ofphysical features)

Mapping

(quantity,relative scale)

Conventional(arbitrary)

Mark position,identify category

(shape, texture color)

Indicate direction(orientation, line)

Express magnitude(saturation, size, length)

Simple symbols and

color codes

graphical representations in the context of UI design Alan Blackwell (2011)



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GraphicResources


Geometric Topological (linking)


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Symbols

elements

Letter forms

Logos andicons

Pictureelements

Connectiveelements

Depictive(pictorial conventions)

Figurative(metonym, visual puns)

Connotative(professional and

cultural association)

Acquired

Texts and symboliccalculi

Diagram elements

Branding

Visual rhetoric

Definition of

regions




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GraphicResources


AlignmentIdentifying shared


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Regions

grids

Borders andframes

Area fills

White space

Gestalt

integration

Containment

Separation

Framing

(composition,photography)

Layering

Identifying sharedmembership

Segregating ornesting multiple

surface conventions

in panels

Accommodatinglabels, captions

or legends




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GraphicResources Correspondence Design Uses

The plane Literal (map) Typographic


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Surfaces

Material objecton which the

marks areimposed

(paper, stone)

Mounting,orientation anddisplay context

Display medium

Euclidean

(scale and angle)

Metrical(quantitative axes)

Juxtaposed or ordered(regions, catalogues)

Image-schematic

Embodied/situated

layouts

Graphs & charts

Relational diagrams

Visual interfaces

Secondarynotations

Signs and displays




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http://worrydream.com/




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Attention

humans can focus– using their perceptual processor –

on a single input channel at a given moment


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Attention

the spotlight could trackmultiple input communication channels

in sequential manner only




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Attention

visual attention is more importantthan the aural one




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Attention

within a input channel,the stimuli are processed in parallel




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Attention

within a input channel,the stimuli are processed in parallel

interferences



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bookpainting

balletpenguin

astringent

melon

Please, indicate the color of each word:



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green yellow white

magentared

blue




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green yellow white

magentared

blue


interferences – the Stroup effect


Cognition


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Cognition

Cognitive processor

the brain compares stimuli and selects a response


human-computer human computer


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human computerinteraction

human-computerinteraction




h u m a n - c o m p u t e r i n t e r a c t i o n



for details see www slideshare net/andresbaravalle/im2044-week-2-lecture Master on Software Engineering :: Human-Computer Interaction

Cognition


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Cognition

Cognitive processor

the decision making process is based onskills, rules, and knowledge


Cognition


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Cognition

Cognitive processor

skill-based decision making

learning by doing: walking, talking, driving, etc.


Cognition


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Cognition

Cognitive processor

skill-based decision making

learning by doing: walking, talking, driving, etc.

influenced by age


Cognition


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Cognition

Cognitive processor

rule-based decision making

e.g., social rules of proper manners

If I’ll see an elderly person, then I’ll politely say “Hello”.


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Cognition


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Cognition

Cognitive processor

knowledge-based decision making

uses the prior background knowledge,even in unfamiliar/unexpected situations


experiment


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Try to compute the two mathematical expressions below:

74 + 46

LXXIV + XLVI

in your headby using a piece of paper

with a calculator


experiment


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Try to compute the two mathematical expressions below:

74 + 46

LXXIV + XLVI

in your headby using a piece of paper

with a calculator

Which is easiest and why?


Cognition


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Cognition

User multi-tasking

attention is a resource that could be sharedin order to perform multiple activities

in the same time


Cognition


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Cognition

User multi-tasking

depends on the activity structure:

multi-modality

e.g., visual + auditory + tactile interaction


Cognition


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Cognition

User multi-tasking


codification

e.g., spatial data versus spoken data


Cognition


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Cognition

User multi-tasking


involved components

using perceptual/cognitive processorversus

motor processor


Cognition


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Cognition

User multi-tasking


involved components

example:playing chess versus pinball


Cognition


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Cognition

User multi-tasking

depends on the difficulty:

well-practiced tasks are more easily performed


Cognition


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Cognition

User multi-tasking

depends on the difficulty:

well-practiced tasks are more easily performed

example: it’s easier to ride the bike on an empty street


Cognition


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Cognition

Motor processor

open-loop control

autonomicsending the same directives to the muscles

no feedback from perceptual processor

cycle time: ~70 ms


Cognition


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Cognition

Motor processor

close-loop control

muscles movements – or their effects – are perceivedand compared to the desired result

cycle time: ~240 ms


Cognition


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Cog t o

multimodal man-machine interaction model (Dumas et al., 2009)


Cognition


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g

cognitive conscious

versus

cognitive unconscious


Property Conscious Unconscious

Engaged by Novelty

EmergenciesRepetition

Expected Events


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Danger Safety

Used in New circumstances Routine situations

Can handle DecisionsNonbranching

tasks

Accepts Logical propositions Logic orinconsistencies

Operates Sequentially Simultaneously

Controls Volition HabitsCapacity Tiny Huge

Persists for Tenths of seconds Decades (lifelong)

Jef Raskin, The Humane Interface, Addison Wesley, 2000


Cognition


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g

Distributed cognition(Edwin Hutchins, 1995)

cognitive resources are shared socially in orderto extend individual cognitive resources or

to accomplish something that an individual agentcould not achieve alone


Cognition

http:/

/hci.ucsd.edu/hutchins/dcogAviation.html


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g


Guidelines (William Gribbons, ‘08)


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( , )

Reading:

place information and instruction in context

employ lists

chunk information

present content in sequence




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( , )

Reading:

maintain consistency in language and procedures

highlight critical/new information, information structure

use familiar terms, and avoid acronyms and jargon


discussion


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Leet (1337) speak

like other languages, 1337 is dynamicsymbols as shapes and not as their meanings

common phonetic replacements + inconsistenciesmany acronyms and random capitalization

M. Perea, J. Duñabeitia, M. Carreiras, “R34D1NG W0RD5 W1TH NUMB3R5”, Journal of Experimental

Psychology: Human Perception and Performance, 2007




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( , )

Reading:

use illustrations to complement text

avoid the gratuitous use of animations & other movement

use familiar typefaces




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( , )

Reading:

allow user control to repeat output

avoid background noise with auditory output

maintain higher contrast


discussion


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www.webpagesthatsuck.com/worst-over-the-top-websites-of-2014.html




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Dr Sabin-Corneliu Buraga – profs info uaic ro/~busaco/

( )

Memory:

leverage existing knowledge, behaviors, and tasks

avoid splitting attention between two tasks

focus on the user goals

limit information and features to what is really needed




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Memory:

partition tasks in reasonably sized groups

support mental calculations, decisions, and comparisons

limit choices




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Memory:

complete information automatically in forms & fields

use mnemonics

minimize screen clutter




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Memory:

provide extra time for tasks

eliminate the anxiety of timeouts

provide reminders



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Dr Sabin-Corneliu Buraga – profs info uaic ro/~busaco/ Master on Software Engineering :: Human-Computer Interaction

Empirical laws


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Fitts’ Law (1954)

predicts the pointing (movement) average timeas a function of the distance to and

width of the given target


Empirical laws


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Fitts’ Law (1954)

how fast we can move the mouse to point toa given UI component having a specific size


Empirical laws


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Fitts’ Law (1954)

T = a + b * log2 (2*D / S)

T = time, D = distance, S = size


Empirical laws


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Fitts’ Law (1954)

consequence:Mac OS X menu is more ergonomic

than the main menu of each window in Windows


Empirical laws


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Fitts’ Law (1954)

consequence:pie (radial) menus are more easily to be used

than pop-up linear ones



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Dr Sabin-Corneliu Buraga – profs info uaic ro/~busaco/ Master on Software Engineering :: Human-Computer Interaction

Empirical laws


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Hick’s Law (1952)

predicts the decision time for a set of choices(e.g., in a menu)


Empirical laws


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Hick’s Law

T = b*log2 (n + 1)

T = average reaction time (entropy of the decision),n = number of equally probably alternatives,b = a constant to be empirically determined


discussion


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Dr Sabin Corneliu Buraga – profs info uaic ro/~busaco/

Hick’s law in the context of wizard (anti)pattern

http://designinginterfaces.com/patterns/wizard/


Empirical laws


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Hick’s Law

example – measuring the user’s reaction time:if a Web page has ~70 links, then the time used to makea choice (i.e., visiting a link) will be ~105 seconds (70*1.5)

the more choices are eliminated,the more enjoyable the experience will be


discussion


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reducing thenumber of choices

Twitter’s frontpage(2010 vs. 2015)


Empirical laws


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Steering Law

predicting the time of moving the hand througha “tunnel” having the length D and width S

S

D


Empirical laws


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Steering Law

T = a + b * D/S

the difficulty varies linearly, not logarithmicguiding the mouse is harder than pointing it


Empirical laws


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Steering Law

consequence: cascading menus are hard to be used


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Persona


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Dr Sabin Corneliu Buraga profs info uaic ro/~busaco/

A precise description of the user of a system,and of what (s)he wishes to accomplish

user archetype

Alan Cooperwww.cooper.com/journal/2008/05/the_origin_of_personas


Persona


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Although personas are not real people,they represent them throughout the design stage

(Blomkvist, 2002)


Persona


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Although personas are not real people,they represent them throughout the design stage

(Blomkvist, 2002)

creates a concrete profile of the typical user(reliable & realistic representations of the key audience segments)


Persona


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Danger of the average users


Persona


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The design must be performed regardingthe necessities and goals of each user category

www.interaction-design.org/encyclopedia/personas.html


Persona


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Important aspects:age, abilities, ethnicity, social context, etc.

user profile


Student Tourist Single (wo)man

N A d G P l R I P


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A. Stanciu, I. Schiopu, A. Grigoriu, I. Lazar (2011) – aisquared.wordpress.com

Name Andreea G. Paul R. Ioana P.

Age 19 32 27Location Iasi, Romania Paris, France Bucharest, Romania

Social Life

For her, going to university

was a challenge because she

had to leave home,

separate from her parents

and beginning the process

of finding her identity, as anadult, and her place in the

world.

Because his earnings are

above average and because

he works at least 50 hours

per week, when he has

some free time he likes tomake the most of it.

She doesn’t have time for

dating wrong persons.

Work Life

She is a student in

her freshman year at

Computer Science.

He works as a software

developer, but now he

is on vacation.

She is a hard working

woman.

Goals

A new city meant new

people & new friends.

That is why she was glad to

take part of a Social Speed

event. From her point of

view, it’s a fun way to

He believes that the person

blesses the place. That is

way when he travels he

thinks it is very important

to know and understand

the residents. Because of

From her point of view, this

application is the perfect

combination between

online dating and real life

dating. She knew from the

start that this was not a


Student Tourist Single (wo)man

N A d G P l R I P


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Name Andreea G. Paul R. Ioana P.

Age 19 32 27Location Iasi, Romania Paris, France Bucharest, Romania

Social Life

For her, going to university

was a challenge because she

had to leave home,

separate from her parents

and beginning the process

of finding her identity, as anadult, and her place in the

world.

Because his earnings are

above average and because

he works at least 50 hours

per week, when he has

some free time he likes tomake the most of it.

She doesn’t have time for

dating wrong persons.

Work Life

She is a student in

her freshman year at

Computer Science.

He works as a software

developer, but now he

is on vacation.

She is a hard working

woman.

Goals

A new city meant new

people & new friends.

That is why she was glad to

take part of a Social Speed

event. From her point of

view, it’s a fun way to

He believes that the person

blesses the place. That is

way when he travels he

thinks it is very important

to know and understand

the residents. Because of

From her point of view, this

application is the perfect

combination between

online dating and real life

dating. She knew from the

start that this was not a

a persona includes a name &a photo, plus some demographics

A. Stanciu, I. Schiopu, A. Grigoriu, I. Lazar (2011) – aisquared.wordpress.com


microdata HTML5 templatehttp://profs.info.uaic.ro/~stefan.negru/personas/


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D S bi C li B f i f i /~b / Master on Software Engineering :: Human-Computer Interaction


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D S bi C li B f i f i /~b /

personas for PaMI (Patient Monitoring Interface) project A. Berila, A. Bulai, C. Chiric & M. Plesca, 2012


Persona


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Remarks (Donald Norman, 2004)

the purpose of the persona is to add empathetic focusto the design

the personas must reflect the target groupfor the design team


Persona


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Main processes:

conduct user researchcondense the research

brainstorm (make classifications)proto-personasrefine

make them realistic

examples: http://uxmag.com/articles/using-proto-personas-for-executive-alignment


discussion


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It’s not important how smart users are;it’s important to be effective

Stephen Turbek, 2011

http://boxesandarrows.com/are-your-users-s-t-u-p-i-d/


discussion


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S.T.U.P.I.D. userStressed Tired Untrained Passive Independent Distracted

S.M.A.R.T. designSimply Memorable A ccept autopilotR ecovery Test in realistic situations

http://boxesandarrows.com/are-your-users-s-t-u-p-i-d/


“Conclusion”


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human capacities, cognition,modeling users, empirical laws, personas



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Documents

Hci02 HumanFactor Users