NUR -Psychological aspects, MHP, GOMS, KLM,...
Transcript of NUR -Psychological aspects, MHP, GOMS, KLM,...
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NUR - Psychological aspects,
MHP, GOMS, KLM, CSCW
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NUR - MODELS
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User interface design - big picture
User Research
step 0
Problem
Description• sources: marketing
research
Application
Domain
step 1
User Modeling• personas
• mental models
User
Requirements• scenarios
• HTA
• storyboards
step 2
UI Design• app. models (GOMS,
KLM, HTA, CTT, etc.)
• prototypes
• final design
• 3 parts
• visual design
• interaction design
• UX design
UI Testing• KLM
• CW
• EE
• user testing (UX)
step 3
Documentation• visual design
• interaction design
• UX design
step 4
Implementation
step 5
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What is a model?
� A model is5
– a simplification of reality
� A model is5
– useful only if it helps in designing, evaluating, or
otherwise providing a basis for understanding the
behavior of a complex artifact such as a computer
system
� To be useful, a model must be5
– simpler than the behavior it models (i.e., extremely
complex models are of questionable value)
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Cognitive Modeling: Definition
� A theory that produces a computational model of
how people perform tasks and solve problems by
using psychological principles and empirical
studies.
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Cognitive Modeling: Role
� Limits the design space
� Answers specific design decisions
� Estimates total task time
� Estimates training time
� Identifies complex, error-prone stages of the
design
� A means of testing current psychological theories
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HOW TO MODEL HUMANSHOW TO MODEL HUMANS
Název prezentace, konference, apod.
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Model Human Processor (MHP)
Card, Moran & Newell (1983)
� most influential model of user
interaction
– used in GOMS analysis
� 3 interacting subsystems
– cognitive, perceptual & motor
– each with processor & memory
• described by parameters (e.g., capacity, cycle
time)
– serial & parallel processing
Adapted from slide by Dan Glaser
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MHP
� Input/output
� Processing
– serial action• pressing key in response to light
– parallel perception• driving, reading signs & hearing
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MHP data
� Based on empirical data
– word processing in the ‘70s
� Processors have
– cycle time (τ)
� Memories have
– storage capacity (µ)
– decay time of an item (δ)
– info code type (κ)• physical, acoustic, visual & semantic
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Perceptual Subsystem Parameters
� Processor
– cycle time (τ) = 100 msec
� Visual Image Store
– storage capacity (µ) = 17 letters
– decay time of an item (δ) = 200 msec
– info code type (κ) = physical• physical properties of visual stimulus
e.g., intensity, color, curvature, length
� Auditory Image Store
– similar parameters
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Memory
There are three types of memory function:
Sensory memories
Short-term memory or working memory
Long-term memory
Selection of stimuli governed by level of arousal.
Attention
Rehearsal
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Sensory memory
� Buffers for stimuli received through senses
– iconic memory: visual stimuli
– echoic memory: aural stimuli
– haptic memory: tactile stimuli
� Examples
– “sparkler” trail
– stereo sound
� Continuously overwritten
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Short-term memory (STM)
� Scratch-pad for temporary recall
– rapid access ~ 70 ms
– rapid decay ~ 200 ms
– limited capacity – 3 ± 1 chunks
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Examples
212348278493202
0121 414 2626
HEC ATR ANU PTH ETR EET
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BrownBrown--Peterson task (about forgetting)Peterson task (about forgetting)
� Subjects presented with trigram (XQJ)
� Experimenter presenters number (257)
� Subject counts backwards by 3’s (2/sec)
� After several seconds, subjects recall trigram
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Other memory testOther memory test
ShepardShepard & & TehgtsoonianTehgtsoonian (1961)(1961)
�� Presented 200 3Presented 200 3--digit numbers in a row.digit numbers in a row.
�� E.g. E.g. 55 492, 865, 931, 758492, 865, 931, 75855 865865, , 55
�� Task: report when you hear a repeated numberTask: report when you hear a repeated number
Název prezentace, koference, apod.
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Memory processesMemory processes
Say the following list of words once to yourself, and Say the following list of words once to yourself, and
then, immediately thereafter, try to recall all the then, immediately thereafter, try to recall all the
words, in any order, without looking back at them:words, in any order, without looking back at them:
Table, cloud, book, tree, shirt, cat, light, bench, Table, cloud, book, tree, shirt, cat, light, bench,
chalk, flower, watch, bat, rug, soap, pillowchalk, flower, watch, bat, rug, soap, pillow
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Long-term memory (LTM)
� Repository for all our knowledge
– slow access ~ 1/10 second
– slow decay, if any
– huge or unlimited capacity
� Two types
– episodic – serial memory of events
– semantic – structured memory of facts, concepts, skills
semantic LTM derived from episodic LTM
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Long-term memory (cont.)
� Semantic memory structure
– provides access to information
– represents relationships between bits of information
– supports inference
� Model: semantic network
– inheritance – child nodes inherit properties of parent nodes
– relationships between bits of information explicit
– supports inference through inheritance
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LTM - semantic network
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Models of LTM - Frames
� Information organized in data structures
� Slots in structure instantiated with values for instance of data
� Type–subtype relationships
DOG
Fixed
legs: 4
Default
diet: carniverous
sound: bark
Variable
size:
colour
COLLIE
Fixed
breed of: DOG
type: sheepdog
Default
size: 65 cm
Variable
colour
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Models of LTM - Scripts
Model of stereotypical information required to interpret situation
Script has elements that can be instantiated with values for context
Script for a visit to the vet
Entry conditions: dog ill
vet open
owner has money
Result: dog better
owner poorer
vet richer
Props: examination table
medicine
instruments
Roles: vet examines
diagnoses
treats
owner brings dog in
pays
takes dog out
Scenes: arriving at reception
waiting in room
examination
paying
Tracks: dog needs medicine
dog needs operation
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PROCEDURAL KNOWLEDGEPROCEDURAL KNOWLEDGE
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Models of LTM - Production rules
Representation of procedural knowledge.
Condition/action rules
if condition is matched
then use rule to determine action.
IF dog is wagging tail
THEN pat dog
IF dog is growling
THEN run away
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LTM - Storage of information
� rehearsal
– information moves from STM to LTM
� total time hypothesis
– amount retained proportional to rehearsal time
� distribution of practice effect
– optimized by spreading learning over time
� structure, meaning and familiarity
– information easier to remember
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LTM - Forgetting
decay– information is lost gradually but very slowly
interference– new information replaces old: retroactive interference
– old may interfere with new: proactive inhibition
so may not forget at all memory is selective 5
5 affected by emotion – can subconsciously `choose' to forget
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LTM - retrieval
recall – information reproduced from memory can be assisted by cues, e.g.
categories, imagery
recognition– information gives knowledge that it has been seen before
– less complex than recall - information is cue
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LTM - retrieval
�� RecognitionRecognition–– You see or hear a stimuli which helps you retrieve info from LTMYou see or hear a stimuli which helps you retrieve info from LTM
�� RecallRecall–– You have to retrieve info from LTM without a specific stimuliYou have to retrieve info from LTM without a specific stimuli
�� Which is easier?Which is easier?
�� Implications for UI design?Implications for UI design?
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Memory structureMemory structure
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Four Major Cognitive ProcessesFour Major Cognitive Processes
1.1. Selective AttentionSelective Attention
2.2. LearningLearning
3.3. Problem SolvingProblem Solving
4.4. LanguageLanguage
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1 Selective Attention1 Selective Attention
�� We can focus on one particular thingWe can focus on one particular thing–– Cocktail party chitCocktail party chit--chat chat -- hearing key words can shift our attentionhearing key words can shift our attention
–– Driving while talking Driving while talking -- take customary route, may not be where are take customary route, may not be where are
goinggoing
�� Prominent visual cues can facilitate selective Prominent visual cues can facilitate selective
attentionattention–– Examples?Examples?
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2 Learning2 Learning
�� Two typesTwo types
–– Procedural Procedural –– How to do somethingHow to do something
–– Declarative Declarative –– Facts about somethingFacts about something
�� InvolvesInvolves
–– MemorizationMemorization
–– Understanding concepts & rulesUnderstanding concepts & rules
–– Acquiring & automating motor skillsAcquiring & automating motor skills•• Swimming, Bike riding, Typing, Writing. TennisSwimming, Bike riding, Typing, Writing. Tennis
•• Driving to work Driving to work
Even when donEven when don’’t want tot want to
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2 Learning2 Learning
�� FacilitatedFacilitated
–– By structure & organizationBy structure & organization
–– By similar knowledge, as in consistency in UI designBy similar knowledge, as in consistency in UI design
–– By analogyBy analogy
–– If presented in incremental unitsIf presented in incremental units
–– RepetitionRepetition
�� HinderedHindered
–– By previous knowledgeBy previous knowledge•• Try moving from Mac to WindowsTry moving from Mac to Windows
=> Consider user=> Consider user’’s previous knowledge in your s previous knowledge in your interface designinterface design
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3 Problem Solving3 Problem Solving
�� Storage in LTM, then applicationStorage in LTM, then application
�� ReasoningReasoning
–– Deductive Deductive -- If A, then BIf A, then B
–– Inductive Inductive -- Generalizing from previous cases to learn about new onesGeneralizing from previous cases to learn about new ones
–– AbductiveAbductive -- Reasoning from a fact to the action or state that caused itReasoning from a fact to the action or state that caused it
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4 Language4 Language
�� RuleRule--basedbased
–– How do you make plurals?How do you make plurals?
�� ProductiveProductive
–– We make up sentencesWe make up sentences
�� KeyKey--word and positionalword and positional
–– PatternsPatterns
�� Should systems have natural language Should systems have natural language
interfaces?interfaces?
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The Left and Right BrainThe Left and Right Brain
Left BrainLeft Brain
�� WordsWords
�� AnalysisAnalysis
�� LogicLogic
�� SequentialSequential
�� Simple TasksSimple Tasks
�� Must be TaughtMust be Taught
Right BrainRight Brain
�� Images and PatternsImages and Patterns
�� Overall SituationOverall Situation
�� Spatial RelationshipsSpatial Relationships
�� Parallel ProcessingParallel Processing
�� Complex ScenesComplex Scenes
�� No Teaching RequiredNo Teaching Required
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CONSEQUENCES FOR UI DESIGNCONSEQUENCES FOR UI DESIGN
Some important properties of human Some important properties of human
brainbrain
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The Left and Right BrainThe Left and Right Brain
�� Graphical User Interfaces have been successful Graphical User Interfaces have been successful
because they use both sides of the brain, using because they use both sides of the brain, using
more of our potential processing capacity. more of our potential processing capacity.
�� A GUI makes use of the right lobe for the strong A GUI makes use of the right lobe for the strong
visual representation of the interface on the visual representation of the interface on the
desktop, and the left lobe for the analysis and logic desktop, and the left lobe for the analysis and logic
that follows.that follows.
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Success of the GUISuccess of the GUI
�� GUIGUI has been such a success and not just a flash in has been such a success and not just a flash in the pan due to the detailed the pan due to the detailed study of user study of user psychologypsychology. .
�� User psychology helps us to understand the other User psychology helps us to understand the other side of the equation from just the machine, by looking side of the equation from just the machine, by looking at the at the whole system whole system -- hardware, software and hardware, software and the the useruser. .
�� MenuMenuss are more are more suited to human processingsuited to human processing, as a , as a GUI calls on both sides of the brain.GUI calls on both sides of the brain.
�� A major rationale for the desktop metaphor is that A major rationale for the desktop metaphor is that humans are humans are better at the recognitionbetter at the recognition of visually of visually grouped things than the recollection of detail.grouped things than the recollection of detail.
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MODELS OF HUMAN MODELS OF HUMAN
BEHAVIORBEHAVIOR
(40)
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Task modelsTask models –– and their purposeand their purpose
(41)
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GOMS
� Goals, Operators, Methods, Selection
Rules
– Developed by Card, Moran and Newell
� Probably the most widely known and used
technique in this family
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GOMS
� Explicit task structure
– hierarchy of goals & sub-goals
� Methods - sequences of actions (operators)– based on error-free expert
– may be multiple methods for accomplishing same goal
• e.g., shortcut key or menu selection
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GOMS
� Selection rules - rules for choosing appropriate
method
method predicted based on context
� Example: when more methods for accomplishing
some subtask – we have to use some kind of
strategy to choose appropriate method – e.g.
deleting one or more characters
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GOMS
� Analysis of explicit task structure
– add parameters for operators
• approximations (MHP) or empirical data
• single value or parameterized estimate
– predict user performance
• execution time (count statements in task structure)
• short-term memory requirements (stacking depth of task
structure)
– benefits
• apply before implementation (comparing alternative designs)
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GOMS - quick example
� Goal (the big picture)
– go from hotel to the airport
� Methods (or subgoals)?
– walk, take bus, take taxi, rent car, take train
� Operators (or specific actions)
– locate bus stop; wait for bus; get on the bus;...
� Selection rules (choosing among methods)?
– Example: Walking is cheaper, but tiring and slow
– Example: Taking a bus is complicated abroad
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GOMS: Goals
� Something the user wants to achieve
� Examples?
– go to airport
– delete file
– create directory
� Hierarchical structure
– may require many subgoals
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GOMS: Methods (Subgoals)
� Sequence of steps to accomplish a goal
– goal decomposition
– can include other goals
� Assumes method is learned & routine
� Examples
– drag file to trash
– retrieve command from long-term memory
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GOMS: Methods (Subgoals)
� Sequence of operators (procedures) for accomplishing a goal (may be multiple)
� Example: Select sentence– Move mouse pointer to first word
– Depress button
– Drag to last word
– Release
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GOMS: Operators
� Specific actions (small scale or atomic)
� Lowest level of analysis– can associate with times
� Examples– Locate icon for item on screen
– Move cursor to item
– Hold mouse button down
– Locate destination icon
– User reads the dialog box
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GOMS: Operators
� Basic actions available for performing a task
(lowest level actions)
� Examples: move mouse pointer, drag, press key,
read dialog box, 5
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GOMS: Selection Rules
� If > 1 method to accomplish a goal, Selection rules
pick method to use
� Examples
– IF <condition> THEN accomplish <GOAL>
– IF <car has automatic transmission> THEN <select
drive>
– IF <car has manual transmission> THEN <find car with
automatic transmission>
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GOMS Output
� Execution time
– add up times from operators
� Assumes
– experts!
� Rank ordering
� Absolute accuracy 10-20%
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Assumptions for GOMS
� “Expert” is performing UI operations
� Interacting with system is problem solving
� Decompose into subproblems
� Determine goals to attack problem
� Know sequence of operations used to achieve the
goals
� Timing values for each operation
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GOMS: Limitations
� GOMS is not for
– Tasks where steps are not well understood
– Inexperienced users
� Why?
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�� Generate task descriptionGenerate task description
–– Pick highPick high--level user Goallevel user Goal
–– Write Method for accomplishing Goal Write Method for accomplishing Goal -- may invoke may invoke
subgoalssubgoals
–– Write Methods for Write Methods for subgoalssubgoals
•• This is recursiveThis is recursive
•• Stop when Operators are reachedStop when Operators are reached
�� Evaluate description of taskEvaluate description of task
�� Apply results to UIApply results to UI
�� IterateIterate
GOMS Analysis – How To
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GOMS Procedure
� Walk through sequence of steps
� Assign each an approximate time duration
� -> Know overall performance time
� (Can be tedious)
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GOMS Example: Retrieve article
� Retrieve the article entitled “Why GOMS?” written
by Bonnie John, 1995, in ACM DL
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• Goal: Retrieve article from ACM DL– Goal: Go to ACM
• Goal: Enter ACM URL
• Goal: Submit URL
– Goal: Go to DL• Goal: Locate DL link
• Goal: Select the link
– Goal: Select method• [Method: Search method
• Goal: Search for article– Goal: Enter search parameters
– Goal: Submit search
– Goal: Identify article from results
• Goal: Select the article]
• [Method: Browse method - <take home exercise>]
– Goal: Save article to disk• Goal: Initiate save action
• Goal: Select location
• Goal: save article to that location
GOMS Example: Retrieve article (cont.)
� Goal structure
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GOMS example: Delete a word
� Goal: delete a word in a sentence.
� Method #1: use the menu
– Recall that the word has to be highlighted.
– Recall that the command is “cut”.
– Recall that “cut” is in the Edit Menu.
– Accomplish goal of selecting and executing “cut”.
– Return: goal accomplished.
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GOMS example: Delete a word (cont.)
� Method #2: use the delete key
– Recall where to position cursor in relation to word to be
deleted.
– Recall which key is delete key.
– Press “delete” key to delete each letter.
– Return: goal accomplished.
� Operators used in these methods
– Click mouse, Drag cursor over text, Select menu, Move
cursor, Press KB key, Think, ...
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GOMS example: Delete a word (cont.)
� Selection rules:
– Use mouse/menu method (#1) if there’s a lot of text to
delete.
– Else use “delete” key (method #2).
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KLM (a low-level variant of GOMS)
� Keystroke Level Model (KLM)
� Simple, but accurate. Widely used.
� Scope:
– skilled users
– doing a task error-free.
– using a specific method in a UI.
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KLM Operators
� User Operators:
– K (keystroke), P (point), H (homing), D (drawing), M
(mental: think).
– Times for each are provided to you • based on extensive research/empirical data.
� System Operator:
– R (respond).
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Procedure
� How KLM works
– Assigns times to different operators
– Plus: Rules for adding M’s (mental preparations) in
certain spots
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KLM = subset of GOMS
� Six keystroke-level primitive operators
K - press a key or button
P - point with a mouse
H - home hands
D - draw a line segment
M - mentally prepare to do an action
R - system response time
� No selections
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Example
1. Select sentenceReach for mouse H 0.40Point to first word P 1.10Click button down K 0.60Drag to last word P 1.20Release K 0.60
3.90 secs
2. Cut sentencePress, hold ^ Point to menuPress and release ‘x’ or Press and hold mouseRelease ^ Move to “cut”
Release
3. ...
Move Sentence
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Current Design: Delete a file by dragging it to the trash
icon
1. Point to file icon (P)
2. Press & hold mouse button (B)
3. Drag file to trash icon (P)
4. Release mouse button (B)
5. Point to original window (P)
3P + 2B = 3.5 sec.
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New Design: Adding a command to menu
1. Point to file icon (P)
2. Click button (BB)
3. Point to file menu (P)
4. Press and hold button (B)
5. Point to delete command (P)
6. Release mouse button (B)
7. Point to original window (P)
4P + 4B = 4.8 sec.
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Assumptions
� These previous scenarios work only work if the
user is currently able to view all the needed
windows and icons.
� If the trash icon for example is buried under other
windows the first procedure is slowed down quite a
bit.
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Inserting Mental Operators:
Where does the user stop and think?
1. Initiating a process.
2. Making strategic decisions.
3. Retrieving a chunk from user’s short term
memory
4. Finding something on the screen.
5. Verifying intended action is complete.
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Mental Operators - New vs Experienced Users
� New users stop and check feedback after every
step
� New users have small chunks
� Experienced users have elaborate chunks
� Experienced users may overlap mental operators
with physical operators
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Delete a file by dragging icon to trash
1. Initiate delete. (M)
2. Find file icon. (M)
3. Point to file icon. (P)
4. Press & hold button. (B)
5. Verify icon reverse video. (M)
6. Find trash icon. (M)
7. Drag file to trash icon. (P)
8. Verify trash reverse video. (M)
9. Release button. (B)
10. Verify bulging trash icon. (M)
11. Find original window. (M)
12. Point to window. (P)
3P + 2B + 7M = 12.6 sec.
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Description Operator Duration (sec)
Mentally Prepare M 1.35
Move cursor to “quick” P 1.10
Double-click mouse button K 0.40
Move cursor to “brown” P 1.10
Shift-click mouse button K 0.40
Mentally Prepare M 1.35
Move cursor to Edit Menu P 1.10
Click mouse button K 0.20
Move cursor to Cut menu item P 1.10
Click mouse button K 0.20
Mentally Prepare M 1.35
Move cursor to before “fox” P 1.10
Click mouse button K 0.20
Mentally Prepare M 1.35
Move cursor to Edit menu P 1.10
Click mouse button K 0.20
Move cursor to Paste menu item P 1.10
Click mouse button K 0.20
TOTAL PREDICTED TIMETOTAL PREDICTED TIME 14.9014.90
Method Used
Cut-and-paste-using-menus
1
2
3
4
5
M=1.35
P=1.10
K=0.20
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Other human features
� Besides time “constants” we have to take into
account also other features
� E.g. when we perform a task repeatedly – we get
better and better (time necessary shrinks)
� Besides MHP we have to use additional “rule”
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Power Law of Practice
Task time on the nth trial follows a power law
• Tn = T1 n-a, where a = 0.4
• i.e., you get faster the more times you do it!
• applies to skilled behavior (perceptual & motor)
• does not apply to knowledge acquisition or quality
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Hick’s Law
� Time it takes for a user to make a decision.
� Given n equally probable choices, the average
reaction time T required to choose among them:
T = b log2(n + 1)
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Fitts’s Law
� Time T to move your hand to a target of size S at
distance D away isT = a + b log (D/S +1)
– Index of difficulty: log (D/S +1)
S is in direction of motion (“length” arbitrary)
Note that distance is between center points
D
S
start
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Název prezentace, konference, apod.
Fitts’ Law
Models movement time for selection tasks
The movement time for a well-rehearsed selection task: • increases as the distance to the target increases• decreases as the size of the target
increases
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Název prezentace, konference, apod.
Fitts’ Law
Time (in msec) = a + b log2(D/S+1)
wherea, b = constants (empirically derived)D = distanceS = size
ID is Index of Difficulty = log2(D/S+1)
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Název prezentace, konference, apod.
Fitts’ Law
Same ID → Same Difficulty
Target 1 Target 2
Time = a + b log2(D/S+1)
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Název prezentace, konference, apod.
Fitts’ Law
Smaller ID → Easier
Target 2Target 1
Time = a + b log2(D/S+1)
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Název prezentace, konference, apod.
Fitts’ Law
Larger ID → Harder
Target 2Target 1
Time = a + b log2(D/S+1)
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Název prezentace, konference, apod.
Determining Constants for Fitts’ Law
� To determine a and b– design a set of tasks with varying values for D and S
(conditions)
� For each task condition – multiple trials conducted and the time to execute each is
recorded and stored electronically for statistical analysis
� Accuracy is also recorded– either through the x-y coordinates of selection or
– through the error rate — the percentage of trials selected with the cursor outside the target
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Název prezentace, konference, apod.
A Quiz Designed to Give You Fitts
http://www.asktog.com/columns/022DesignedToGiveFitts.html
Microsoft Toolbars offer the user the
option of displaying a label below each
tool. Name at least one reason why
labeled tools can be accessed faster.
(Assume, for this, that the user knows
the tool.)
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Název prezentace, konference, apod.
A Quiz Designed to Give You Fitts
1. The label becomes part of the target. The
target is therefore bigger. Bigger targets, all
else being equal, can always be acccessed
faster, by Fitt's Law.
2. When labels are not used, the tool icons
crowd together.
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Slide adapted from Pourang Irani
A Quiz Designed to Give You FittsA Quiz Designed to Give You Fitts
You have a palette of tools in a You have a palette of tools in a
graphics application that consists graphics application that consists
of a matrix of 16x16of a matrix of 16x16--pixel icons pixel icons
laid out as a 2x8 array that lies laid out as a 2x8 array that lies
along the leftalong the left--hand edge of the hand edge of the
screen. Without moving the array screen. Without moving the array
from the leftfrom the left--hand side of the hand side of the
screen or changing the size of the screen or changing the size of the
icons, what steps can you take to icons, what steps can you take to
decrease the time necessary to decrease the time necessary to
access the average tool?access the average tool?
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Slide adapted from Pourang Irani
A Quiz Designed to Give You FittsA Quiz Designed to Give You Fitts
1.1. Change the array to 1X16, so all Change the array to 1X16, so all
the tools lie along the edge of the tools lie along the edge of
the screen.the screen.
2.2. Ensure that the user can click Ensure that the user can click
on the very first row of pixels on the very first row of pixels
along the edge of the screen to along the edge of the screen to
select a tool. There should be select a tool. There should be
no buffer zone. no buffer zone.
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Fitts’ Law Example
� Which will be faster on average?– pie menu (bigger targets & less distance)
Today
Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Pop-up Linear Menu Pop-up Pie Menu
Source: Landay, James. “Human Abilities”. CS160 UC Berkeley.
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Fitt’s Law in Microsoft Office 2007
Larger, labeled controls can
be clicked more quickly
Mini Toolbar: Close to the cursor
Magic Corner: Office Button
in the upper-left corner
Source: Jensen Harris, An Office User Interface Blog : Giving You Fitts. Microsoft, 2007.
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Motor: Key Input
� Parameters of keyboard input based on
– Skill of the typist• Best Typist (120 wpm): 80 msec
• Worst Typist: 1200 msec
– Predictability & continuity of the text to be typed• Typing random letters: 500 msec
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Motor: Mouse Movement
� Fitts’s Law is a robust
predictor of mouse
movement
� Sometimes distance
metric is not clear-cut
– Nested menus
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Motor: Applying Fitts’s Law
� Fitts’s law recommends
– Larger target sizes
– Smaller distances to targets
– Usage of corners and edges (they have “infinite”
height and width)• Macintosh menus are faster than Windows/Unix style menus
because they lie on the screen edge
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Motor: Fisheye Model
� Provide local context against a global context
� Focuses on screen space versus user’s attention
� 3 properties
– Focal point
– Distance from focus, D
– Level of detail, LOD
� Degree of Interest
– Function to determine whether to display an item or not and its size
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Motor: Fisheye Menu
� Good for browsing tasks
� Allows one to present entire menu without having to use hierarchies or scrolling
� Longer learning curve
� http://www.cs.umd.edu/hcil/fisheyemenu/fisheyemenu-demo.shtml
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Motor: Hand Movements
� Switching between keyboard and mouse
– ≈ 360 msec
� Differences in times due to distance from home
position on keyboard and the size of the targets
– Joystick ≈ 260 msec
– Arrow keys ≈ 210 msec
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Perception
� Recognition or perception
– Measure the time to respond to stimuli• Responding to lights
• Recognizing words
� Saccade: fast movement of eye, head, etc.
– Measure the time to move and take in information in
each jump • Eye jerking around, scanning or moving to the next location
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Perception
� An example: spreadsheet perception
� Looking for cell addresses and retrieving data
230 msec
130 msec, store row label
230 msec
130 msec, store col label
230 msec
1350 msec, retrieve row & col label
Total: 2300 msec
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Summary of Cognitive Parameters
Retrieve from memory 1200 msec
Execute a mental step 70 msec
Choose among methods 1250 msec
Enter a keystroke 230 msec
Point with a mouse 1500 msec
Move hands to mouse 360 msec
Perceive 100 msec
Make a saccade 230 msec
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DESCRIPTIVE MODEL DESCRIPTIVE MODEL –– AN AN
EXAMPLEEXAMPLE
Název prezentace, konference, apod.
(100)
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Guiard’s Model of Bimanual Skill
� Rationale
– Humans are not only two-handed, they use their hands
differently
– Studying the between-hand division of labour in
everyday tasks reveals that most tasks are asymmetric
(I.e., our hands have different roles and perform
distinctly different tasks)
� Guiard’s model5
– identifies the roles and actions of the non-preferred
(non-dominant) and preferred (dominant) hands (next
slide)
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Guiard’s Model of Bimanual Skill (2)
A pretty picture might help (next slide)
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Guiard’s Model of Bimanual Skill (3)
Non-preferred hand
• leads the preferred
hand
•sets the spatial frame
of reference for the
preferred hand
•performs coarse
movements
Preferred hand
• follows the non-
preferred hand
•works within
established frame of
reference set by the
non-preferred hand
•performs fine
movements
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Scrolling Using the Non-Preferred Hand
Thank you Microsoft (next slide)
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Microsoft Office Keyboard
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CSCWCSCW
Název prezentace, konference, apod.
(106)
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What is CSCW?What is CSCW?
�� WorkWork is a social activityis a social activity–– People and their activities are integral to design of technologyPeople and their activities are integral to design of technology
�� Workers may have social proximity despite Workers may have social proximity despite
physical/temporal distance.physical/temporal distance.
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CSCW focuses on people working with othersCSCW focuses on people working with others
CommunityOrganizatio
nProject
teamsSmall
groupsIndividuals
traditiona
l HCI
CSCW HCI
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Face to Face communicationFace to Face communication
�� Personal SpacePersonal Space
�� Eye contact and gazeEye contact and gaze
–– Can convey interest, confusion, boredomCan convey interest, confusion, boredom
�� Gestures and body languageGestures and body language
�� Back channels, confirmation, interruptionBack channels, confirmation, interruption
–– Back channels = nods, shrugs, small noisesBack channels = nods, shrugs, small noises
�� Turn TakingTurn Taking
–– Ums, ahs, pausesUms, ahs, pauses
�� What happens when these channels are What happens when these channels are
unavailable?unavailable?
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Communication
CoordinationFace to Face
Information
Communication
Coordination
Information
Computer
mediated
interaction
Face to Face vs CMIFace to Face vs CMI
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Dimensions of CooperationDimensions of Cooperation
Same Place Different Place
Same
Time
Synchronous
Local
Synchronous
Remote
Different
Time
Asynchronous
Local
Asynchronous
Remote
Time
Location
What are some examples of applications in these areas?
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Dimensions of CooperationDimensions of Cooperation
Same Place Different Place
Same
Time
Face to Face
conversation
Telephone
Different
Time
Post-it note Letter
Time
Location
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Collaborative SituationsCollaborative Situations
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Remote AsynchronousRemote Asynchronous
Usenet
Wikis
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Remote SynchronousRemote Synchronous
Instant messaging
Videoconferencing
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CoCo--located Synchronouslocated Synchronous
Smart Classrooms
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CoCo--located Asynchronouslocated Asynchronous
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Groupware systemsGroupware systems
�� EmailEmail
�� VideoconferencingVideoconferencing
�� Lotus NotesLotus Notes
�� Bulletin BoardsBulletin Boards
�� Google DocumentsGoogle Documents
�� 55
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EmailEmail
�� Where does it fit?Where does it fit?
�� Why is it successful? Where has it failed?Why is it successful? Where has it failed?
Same Different
Same Synchronous
Local
Synchronous
Remote
Different Asynchronous
Local
Asynchronous
Remote
Time
Place/Space
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VideoconferencingVideoconferencing
�� Where does it fit?Where does it fit?
�� Why isnWhy isn’’t it more popular?t it more popular?
Same Different
Same Synchronous
Local
Synchronous
Remote
Different Asynchronous
Local
Asynchronous
Remote
Time
Place/Space
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Challenges for design in CSCWChallenges for design in CSCW
�� What is CSCW and why is it important?What is CSCW and why is it important?–– Study of the various ways that individuals work in groups and Study of the various ways that individuals work in groups and
the technologies (hardware and software).the technologies (hardware and software).
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Select Findings in CSCW (Ackerman)Select Findings in CSCW (Ackerman)
�� Exceptions tend to be the norm in work processesExceptions tend to be the norm in work processes
�� People prefer to know who else is present in a shared space, andPeople prefer to know who else is present in a shared space, andhow they are performinghow they are performing
�� Visibility of communication and information exchange can enable Visibility of communication and information exchange can enable learning, but also works against efficiency under some learning, but also works against efficiency under some circumstances.circumstances.
�� Norms emerge for CSCW systems, and these norms tend to be Norms emerge for CSCW systems, and these norms tend to be constantly reconstantly re--negotiated. negotiated.
�� Critical Mass problemsCritical Mass problems
�� Importance of Incentives (tied to many other issues above)Importance of Incentives (tied to many other issues above)
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CSCW as a CSCW as a ‘‘Science of the ArtificialScience of the Artificial’’
�� ““CSCW is at once an engineering discipline CSCW is at once an engineering discipline
attempting to construct suitable systems for attempting to construct suitable systems for
groups, organizations, and other collectivities, groups, organizations, and other collectivities,
and ad the same time, CSCW is a social and ad the same time, CSCW is a social
science attempting to understand the basis for science attempting to understand the basis for
that construction in the social worldthat construction in the social world””•• Ackerman (2000: 13)Ackerman (2000: 13)
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Název prezentace, konference, apod.
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Thank you for your attention