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3. Formal models for design: details of conceptual modeling

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3. Formal models for design: details of conceptual modeling The modeling zoo of HCI (Streitz) Competence models Process models Dialogue models The modeling zoo of HCI ... – PowerPoint PPT presentation

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Title: 3. Formal models for design: details of conceptual modeling


1
3. Formal models for design details of
conceptual modeling
  • The modeling zoo of HCI (Streitz)
  • Competence models
  • Process models
  • Dialogue models

2
The modeling zoo of HCI (Streitz)
  • In HCI there are many different approaches,
    depending on the goals of modeling, the use of
    the models, and the topic to be modeled.
  • Why formal conceptual models?
  • Conceptual models are for design decisions (team
    work)
  • Conceptual models are for implementation
    (engineers)
  • Evaluation models are for evaluation - answering
    design questions (design experts, client of
    design, and users)
  • Formal models enable documentation
  • This is valid for all models discussed here The
    design view (Moran)

3
Competence models
  • The Psychological view
  • Models to understand the users view of the
    interaction. Used to analyze and specify
    knowledge the user needs in order to apply the
    system.
  • Helps assessing the usability of the system
  • GOMS
  • CCT
  • Action Language

4
GOMS
  • Card, Moran, Newell
  • simulation of interaction
  • simplification of cognitive processes
  • prototype user
  • goals
  • operators
  • methods
  • selection rules

5
GOMS, example
  • Goals
  • GOAL INVITE-KEYNOTE-SPEAKER
  • GOAL IDENTIFY-RELEVANT-EXPERTS
  • GOAL FIND-COORDINATES(expertname)
  • GOAL COLLECT-INFO-SOURCES
  • GOAL VERIFY-ADDRESS
  • Operators
  • GET-FROM-REFERENCE-LIST(expertname,
    list-of-keywords)
  • WEBSEARCH(expertname, coordinates)
  • GET-FROM-SOURCE-INFO(documentdate)
  • Methods
  • ONE-AT-THE-TIME-METHOD
  • REDUCE-SOURCELIST
  • CHECK-SOURCE-DATE
  • Selection Rules
  • LONG-LIST-REDUCTION
  • UNFAMILIAR-INSTITUTE-VERIFICATION
  • CONTINGENCY-CHOICE

6
GOMS, example
  • GOAL FIND-COORDINATES(expertname)
  • METHOD
  • ONE-AT-THE-TIME-METHOD
  • GOAL COLLECT-INFO-SOURCES
  • SELECTION-RULES
  • LONG-LIST-REDUCTION
  • if sourcelist length gt5 REDUCE-SOURCELIST
  • UNFAMILIAR-INSTITUTE-VERIFICATION
  • if institute unfamiliar add goal
    VERIFY-ADDRESS
  • CONTINGENCY-CHOICE
  • if number of experts found lt3 add goal
    FIND-COORDINATES(expertname)
  • GOAL VERIFY-ADDRESS
  • METHOD
  • GET-FROM-SOURCE-INFO(documentdate)

7
Cognitive complexity theory (CCT)
  • Kieras Polson (based on GOMS)
  • based on human information processing model
  • production rules
  • to be learned and stored in long term memory
    (LTM)
  • to be used during interaction with the machine,
    through processes in working memory (WM)

8
CCT
  • Production rules in working memory
  • if (condition) then (action)
  • condition test input against content of WM
  • action output content of WM to activity
  • if more than one rule applies priority rule
    (e.g. highest one applies)
  • complexity
  • number of production rules in LTM index for
    learnability
  • number of cycles in WM index for ease of use
  • number of shared productions for 2 systems index
    of transfer

9
CCTExample
  • Check source info
  • user wants to validate address info
  • no telephone available, or address at other
    continent
  • CCT model for novice in web use, and for expert

10
CCT example novice user
  • novice
  • 1. if (and (goal validate source info)
  • 2. (e-mail adress is on current web page))
  • 3. then (and (add goal wait 3 days for email
    answer
  • resume search if failure)
  • 4. (send email for verification))

11
CCT example expert user
  • expert
  • 1. if (and (goal validate source info)
  • 2. (e-mail adress is on current web
    page))
  • 3. then (if document date available in source
    info
  • 4. (reject if date before 1-1-2002))
  • 5. else (and (add goal wait 3 days for email
    answer
  • 6. resume search if failure)
  • 7. (send email for
    verification))

12
Action Language
  • Reisner Psychological BNF
  • analysis focuses on user behavior and
    communication
  • attention on human knowledge and knowledge sources

13
Action Language, example
  • Get web document date USE MUSCLE MEMORY

    (ltretrieve syntax infogt ltinspect dategt)
  • ltretrieve syntax infogt ltretrieve from human
    memorygt ltretrieve
    from external sourcegt
  • ltretrieve from human memorygt RETRIEVE FROM
    LTM
  • RETRIEVE FROM WM
  • ltretrieve from external sourcegt RETRIEVE FROM
    BOOK
  • ASK SOMEONE EXPERIMENT
  • USE ON-LINE HELP
  • ltinspect dategt ltopen document source
    windowgt
  • SCAN WINDOW FOR DATE
  • ltdelete windowgt
  • ltopen document source windowgt PRESS RIGHT
    MOUSE BUTTON
  • POINTER TO DOCUMENT INFO
  • RELEASE MOUSE BUTTON
  • ltdelete windowgt ..

14
Process models
  • The linguistic view
  • Process models aim at understanding the
    interactive process between human and machine,
    over time. These models focus explicitly /
    additionally / on the dialogue.
  • Models may provide estimate of interaction time,
    and estimate of complexity of user behavior in
    interaction with the interface
  • TAG
  • Key-stroke model

15
Task Action Grammar (TAG)
  • Payne and Green
  • focus on learnability and ease of use
  • complexity (number of feature instances to be
    learned) learnability
  • consistency (of rules to be applied) task effort
    / ease of use
  • TAG is a feature grammar
  • TAG does not consider the screen,
    nor the meaning of the
    features

16
Task Action Grammar, example
  • List of commands
  • move cursor one character forward ctrl-C
  • move cursor one character backward meta-C
  • move cursor one word forward ctrl-W
  • move cursor one word backward meta-W
  • List of features possible values
  • direction forward, backward
  • unit character, word
  • dictionary of simple tasks
  • move cursor one character forward directionforw
    ard, unitchar
  • move cursor one character backward directionbac
    kward, unitchar
  • move cursor one word forward directionforward,
    unitword
  • move cursor word backward directionbackward,
    unitword
  • rule schemas
  • task direction, unit ? symbol direction
    letter unit
  • symbol direction forward ? ctrl
  • symbol direction backward ? meta
  • letter unit word ? W
  • letter unit character ? C

17
Key-stroke model
  • Card, Moran, Newell
  • model of sequential user actions and system
    actions
  • estimates of times of actions based on empirical
    measurements
  • estimate of mental actions are very global and
    fixed
  • system reaction times are parameter of model
  • predictions are accurate for errorless behavior,
    at low level of interaction only

18
Key-stroke level model
  • Operator description time (sec)
  • K press key or button (shift or control key
    count separately)
  • best typist (135 wpm) .08
  • good typist (90 wpm) .12
  • average non-secretary typist (40 wpm) .28
  • typing complex codes .75
  • P point with mouse to target on display (Fittss
    Law) 1.10
  • H home hand on keyboard or device .40
  • D(n,l) draw n straight-line segments of total
    length l cm
  • (calculated for a square .56 cm grid) .9n
    .16l
  • M mentally prepare 1.35
  • R(t) system response t

19
Key-stroke level model, example
  • Line editor Poet
  • go to next line MKLINEFEED
  • type substitute command MKS
  • type new word 5Kword
  • end new word MKRETURN
  • type word to be replaced 5Kword
  • end word to be replaced MKRETURN
  • end command KRETURN
  • t(execute) 4 t(M) 15 t(K) 8.4 sec

20
Key-stroke level model, example
  • Direct manipulation editor Bravo
  • take mouse Hmouse
  • point word to be replaced Pword
  • select word Kyellow
  • hand to keyboard Hkeyboard
  • type replace command MKR
  • type new word 5Kword
  • end new word MKESC
  • t(execute) 2 t(M) 8 t(K) 2 t(H) t(P)
    6.2 sec

21
Dialogue models
  • Goal is to provide implementers with a complete
    specification of all that is relevant from the
    users point of view.
  • Model the dialogue between human user and
    machine
  • competence (what should the user know)
  • process (what is the dynamic structure of the
    dialogue)
  • Enables analysis of usability, learnability, and
    completeness of the detail specifications.

22
New User Action Notation (NUAN)
  • Based on Hix Hartson (1993) UAN
  • all overt user actions
  • interface feedback
  • interface state info
  • connection to computation
  • Extended based on needs for specification in
    design practice

23
NUAN considers mental actions
  • RECALL (x from y)
  • RETAIN (x in y)
  • FORGET (x)
  • FIND (x in y)
  • CHOOSE (x from y)
  • etc,
  • where
  • y can be different parts of human memory, or
    located outside the users mind

24
NUAN is a feature grammar
  • POINTERTO(ltanybuttongt) MOVEPOINTER(ltanybuttongt
  • where ltanybuttongt ltyesbuttongtltnobuttongtlt..

25
NUAN
  • has a slot for prior states conditions
  • replaces interface feedback by interface actions
  • is clear on user understood simultaneous vs
    consecutive events
  • allows audio-visual representation of user
    actions, interface actions and interface state

26
Interaction New Email Interaction New Email Interaction New Email Interaction New Email
About About Interface Pre-state Interface Pre-state
This interaction describes the arrival of a new email, in case all previous mails have been read. If another message arrives during this dialogue, the dialogue would re-start immediately after the current one is finished. This interaction describes the arrival of a new email, in case all previous mails have been read. If another message arrives during this dialogue, the dialogue would re-start immediately after the current one is finished. 1. Email running as a background process though not represented at the interface. 2. All previous emails have been read (unread_mail false until the new arrival). 1. Email running as a background process though not represented at the interface. 2. All previous emails have been read (unread_mail false until the new arrival).
User Actions Interface Actions Interface State Connection to Computation
! MESSAGE("New email has arrived") unread_mail gt true
! ASK("Read now",Yes,No) Previous dialogue postponed, not aborted
CHOOSE(task to continue, from the set of previous tasks plus new_email task)
POINTERTO(ltyesbuttongt) CLICK(ltyesbuttongt) -gt InteractionRead Email POINTERTO(ltnobuttongt CLICK(ltnobuttongt) MOVEPOINTER(ltyesbuttongt)SHOW_EMAIL(latest) MOVEPOINTER(ltnobuttongt) unread_mail gt false unread_mail gt true
HIDE_MESSAGE() Previous dialogue enabled to continue
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