Cognitive Psychology Theories for Knowledge Management

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Cognitive Psychology Theories
for Knowledge Management
Tobias Ley, Know-Center
TUG 707.009 Foundations of Knowledge Management
02 Dec 2008/ 2
Overview
What is Cognitive Psychology?
Theories in Cognitive Psychology and Applications in Knowledge
Management
Knowledge Space Theory
Application in the APOSDLE Project
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Cognitive Psychology: What it is
Psychology: The study of Human Behavior

Explanation and Prediction of Human Mental Processes und Behavior

Validation of Theories and Models
Areas

Cognition, Emotions

Social and Group Interactions

Individual Differences and Personality

Organizational & Work, Educational, Clinical, Traffic, Forensic
Cognition

High level functions carried out by the human brain, including comprehension and
formation of speech, visual perception and construction, calculation ability, attention
(information processing), memory, and executive functions such as planning, problem-
solving, and self-monitoring.
Methods

Clinical Diagnostic Findings, Expert-Novice Contrasts, Reaction Time Experiments,
Computational Models, Brain Imaging Techniques
http://www.lhsc.on.ca/programs/msclinic/define/c.htm
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Cognitive Psychology: Why it is
relevant for Knowledge Management
Changing Human Behavior in Organizational Settings

How to design organizational settings to change human behavior?

Effectiveness, efficiency, health, motivation, satisfaction, …
Focussingon the Human Factor in Interacting with Computers

How to design interaction, interfaces and information?

Usability, joy of use, learnability, fault tolerance, …
Focussingon Intelligent Applications

Designing computers to behave like humans

More “intelligent”software applications and agents, adaptivity, …
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Theories and their applications
The role of Working Memory: Cognitive Load and Learning
Long term Memory: Propositions and Associative Networks
Long term Memory: Mental Models and Metaphors
A Structural Model of Knowledge Representation:
Knowledge Space Theory
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Cognitive Load and Learning
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Die Struktur des
Gedächtnisses
Cooper (1998)
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Sensorisches Gedächtnis
Ultrakurze Speicherungsdauer

Visuell (~ 0,5 sec)

Auditiv (~ 3 sec)
Prä-attentiveVerarbeitung

Wahrnehmungsorganisation nach Gestaltgesetzen
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Langzeitgedächtnis
Inhalt:

Wissen und Fertigkeiten
Kapazität:

Prinzipiell unlimitiert
Prozesse

Aktivierung der Inhalte erfolgt über Anfragen des Arbeitsgedächtnisses
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Arbeitsgedächtnis
Inhalte

Getrennte Systeme für auditiv-sprachlicheInhalte (phonologicalloop) und
visuell-bildliche Inhalte (visualsketchpad)
Kapazität

Begrenzte Zahl an Einheiten (<9)

Chunking
Prozesse

Zentrale Rolle des AG für die Enkodierung

Rolle der Aufmerksamkeit
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CognitiveLoadTheory–
Theorie der kognitiven Belastung
Was ist kognitive Belastung?

Maßan mentaler Aktivität, die das Arbeitsgedächtnis in einer bestimmten
Zeiteinheit belastet

Abhängig von der Anzahl der Einheiten, die bewusst verarbeitet werden
muss

CognitiveLoadist nicht gleich Aufgabenschwierigkeit
Beispiel: Merken von Zahlenreihen
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Die Rolle der kognitiven Belastung
beim Lernen
Warum ist bestimmtes Material schwer zu erlernen?
1.
Anzahl an zu lernenden Elementen ist hoch
2.
Zusammenhang zwischen den Elementen ist groß(“Item Interactivity”),
d.h. Elemente können nicht unabhängig von anderen verstanden werden
Beispiel Sprachenlernen

Vokabeln (lowiteminteractivity)

Grammatik (high iteminteractivity)
Beispiel Verwandtschaften (vgl. Cooper, 1998)

Trueorfalse?
„My father‘sbrother‘sgrandfatherismygrandfatrher‘sbrother‘sson“
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Cooper (1998)
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Zwei Arten von kognitiver Belastung
(1)
Aufgaben-inhärent (“intrinsic”)

Nur abhängig von der Schwierigkeit des zu lernenden Stoffs

Zahl und Zusammenhang der Einheiten
Aufgaben-extern (“extraneous”)

Abhängig vom instruktionalenDesign und vom verwendeten Lernmaterial
Cooper (1998)
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Zwei Arten von kognitiver Belastung
(2)
Cooper (1998)
leichterStoff
schwierigerStoff
& unpassendes
Material
schwierigerStoff
& passendes
Material
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Beispiel: Split Attention Effect
Sweller, Chandler,
Tierney & Cooper
(1990)
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LongtermMemory: Propositions and
Associative Networks
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PropositionalenRepräsentationen
beim Textverstehen
{Lincoln; Präsident-von; USA}
{Lincoln; befreien; Sklaven}
{Krieg; bitter}
Anderson (2000)
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Der Aufbau von propositionalen
Repräsentationen beim Textverstehen
Repräsentation ist elementaristisch
Prozess ist additiv
Verknüpfung von Elementen erfolgt im Arbeitsgedächtnis

direkt wenn beide Propositionenim AG repräsentiert sind

schwieriger wenn eine Propositionaus dem LZG abgerufen werden muss

am schwierigsten wenn eine „Lücke“entsteht und eine Inferenz(neue
Proposition) gebildet werden muss
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Spreading Activation Model des Abrufs
ausdemLangzeitgedächtnis Ai
= Bi
+ ΣwjSji
Sji
= 2-log(Fanj)
UntersuchungenzumFächereffekt(“Fan Effect”)
Anderson & Lebiere(1998)
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LongtermMemory: Mental Models &
Metaphors
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Empirische Probleme mit
PropositionalenRepräsentationen Hans war auf dem Weg zur Schule …
An der Kinokasse …
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Der Aufbau vom mentalen Modellen
beim Textverstehen
Holistische analoge Repräsentationsform

i.ggs. zu Propositionenals digitale Repräsentation
Aktivierung von Vorwissen
Elaborationvon „Szenarien“

Skripts, Schemata, Frames
Top-DownVerarbeitung

„Leerstellen“als Fragen an den Text

Informationssuche oder Inferenz
Fortlaufende Evaluation des Mentalen Modells

Übereinstimmung mit dem Text

Plausibilität und Vollständigkeit
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Empirische Belege
Mentale Rotation
Schemata bei Schach-Experten (Chase & Simon, 1973)
Navigationsaufgaben in einer Stadt (Perrig& Kintsch, 1985)
Lernen von Zeitzonen (Schnotz& Bannert, 1999)
Lernen von Technischen Systemen (Mayer, Mathias & Wetzel,
2003)
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Schnotz& Bannert,
2002
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Beispiel: MentaleRepräsentationvon
technischenSystemen
Mayer, Mathias,
& Wetzell(2003)
MentalesModelldes Systems
erlaubt

Bildenvon Inferenzen

Interne mentaleSimulation von
Abläufen

Beantwortungvon
Transferaufgaben
Lernenals2-stufiger Prozess

Zerlegendes Systems in
Teilkomponenten

Bildeneineskausalenmentalen
Modells
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LongtermMemory: Metaphors &
Mental Models
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Metaphernim
Wissensmanagement
ImplizitesWissenüber“Wissen”

WissenalsBibliothek

WissenalsumkämpfterSchatz

WissenalsKanalisationssystem
Moser (2003)
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Knowledge Space Theory
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Overview
Knowledge Space Theory: the fundamentals
A competency based extension: the Competence
Performance Approach
Applying Knowledge Space Theory in modellingfor work-
integrated learning
Three scenariosfor supporting work integrated learning

work-integrated assessment

competency gap analysis

validation
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Knowledge Space Theory –
The Fundamentals
Doignonand Falmagne‘s(1999) intention: „to builtan efficient
machinefortheasessmentof knowledge“
Assessing knowledge of a student in a non-numerical and
qualitative way
Sharp departure from traditional numeric measurement
approaches resembling classical physics
Mathematics in the spirit of current research in combinatorics
with no attempt for obtaining a numerical representation
Starting Point is a possibly large but essentially discrete set of
units of knowledge
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Lookingat thePerson
KnowledgeState of a
Person determinedfrom
theperformancein the
tasks
A knowledgedomaincanbeviewedin
tworespects Lookingat theTasks
Solution Dependencies
withinthetasksof a
domain
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Taskscanbestructuredaccordingto a
PrerequisiteRelationQDomain of knowledge: Collectionof
all tasksin thedomain
SRPrerequisiteRelation capturing
solutiondependenciesin thetasks
in Q
SRisreflexive and transitive
c
ba
Qqr, qr

p
ca
p
cbp
ba
p
Example
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A KnowledgeState describesthe
knowledgeof a person
Example
Q={a,b,c}
K={{},{a},{b},{a,b},{a,b,c}}
c
ba
K

K
K


Q,
QDomain of knowledge: Collectionof
all tasksin thedomain
KKnowledgeState: A subsetof Q
KKnowledgeStructure: The
Collectionof all KnowledgeStates
IfKisclosedunderunion, the
knowledgestructureiscalled
KnowledgeSpace
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KnowledgeSpaceand Prerequisite
Relation: Twosidesof thesamecoin
(Q,K)
K B

a
b
c
d
e
ba
b
a
a
bcd
e
a
(Q, )
p
QXQ⊆p
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Using Knowledge Spaces in Adaptive
Tutoring
Falmagneet al., 2004;
http://www.aleks.com
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Using Knowledge Spaces in Adaptive
Tutoring
Falmagneet al., 2004;
http://www.aleks.com
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Knowledgein a domainismodelledas a setof possibleknowledge
states
A KnowledgeSpacecanbevalidatedbycomparingitto theempirically
observedanswerpatterns
A validKnowledgeSpacecanbeusedforindividualizedand adaptive
knowledgediagnosis
WhatKnowledgeSpaceTheory
cando
(Korossy, 1997)
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Itisonlya descriptivemodelwithoutconsiderationforthe
underlyingcognitiveprocesses
Thereforea transferof thediagosisto othertasksisnot
possible
Givesonlya simple recommendationsforlearninginterventions
WhatKnowledgeSpaceTheory
cannotdo
(Korossy, 1997)
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CompetencyBased
KnowledgeSpaceTheory CompetencePerformance Approach (Korossy, 1993)
Addinga theoreticalcomponentunderlyingtheobservable
solutionbehavior
Knowledgeismodelledas competenceand performance
Competencies: Knowledgeand skillsneededto produce
performance
Competencemodelisderivedfromgeneralordomainspecific
learningtheoriesaboutthedevelopmentof knowledgeand skills
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TheCompetencePerformance
Approach
),(PA
A
∈x
A
P
∈Z
Performance Space
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
x
)( :k
K
A
℘→
)( :p
A
K
℘→
CompetenceSpace
),(K
ε
ε
ε

ε
ε
ε
ε
ε
ε
ε
ε
ε
ε
ε
εε
ε
ε
ε
K
K∈
ε
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Overview
Knowledge Space Theory: the fundamentals
A competency based extension: the Competence
Performance Approach
Applying Knowledge Space Theory in modellingfor work-
integrated learning
Three scenariosfor supporting work integrated learning

work-integrated assessment

competency gap analysis

validation
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Work-integratedLearningwithAPOSDLE
Real Time
Real Place
Real Content
Real Backend Systems
www.aposdle.org
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Modellingfor an Adaptive Technology
Enhanced Learning Environment
Three Models are needed to support adaptivity

Knowledge Base

Student Model

Teaching Model
Albert et al., 2002
Surmise Relation on the set of competencies
Deriving a Competency State from tasks performed
in the past
Using competency as a learning goal to devise educational
interventions (learning events)
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RESCUE: TheLearningDomain “Requirements Engineering”as the learning domain for the first
prototype
RESCUE -Requirements Engineering with Scenarios in User-
CenteredEnvironments(Maiden & Jones, 2004)
An APOSDLE learning environment for requirements engineers
Tasks and Elementary Competencies

Tasks

3_1 Use the findings of the Activity Model (AM) to identify system boundaries
4_2 Model the system's hard and soft goals
4_3 Interpret the AM and integrate the identified actors and goals into the
Strategic Dependency (SD) Model
4_5 Model dependencies between strategic actors for goals to be achieved
and tasks to be performed
4_6 Model dependencies between strategic actors for availability of resources
5_1 Refine the Strategic Dependency Model
5_2 Refine the Strategic Rationale (SR) Models
5_3 Produce an integrated SR Model using dependencies in the SD Model
5_4 Check that each individual SD Model is complete and correct with
stakeholder goals, soft goals, tasks and resources
5_5 Validate the i* SR Model against the SD Model (cross-check)
Competencies

3 Knowledge about the Activity Model and the activity descriptions
12 Knowledge about the Context Model
13 Knowledge about the Strategic Dependency Model (SD-Model)
15 Knowledge about the Strategic Rationale Model (SR-Model)
16 Knowledge of validating the SR Model
20 Ability to produce an i* Model

Task-Competency Assignment

Competencies

Tasks
3 12 13 15 16 20 Minimal Interpretations

3_1 X X X {3, 12, 13}
4_2 X X {15, 20}
4_3 X X X {3, 13, 20}
4_5 X X {13, 20}
4_6 X X {13, 20}
5_1 X {13}
5_2 X {15}
5_3 X X X {13, 15, 20}
5_4 X X X {13, 15, 16}
5_5 X X X {13, 15, 16}

Task Competency Assignment provides the
basis for
1.Competence Performance Structure
2.Prerequisite Relation on the set of
competencies
Ley et al. (2006)
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Competence Performance Structure
(Example)
Ley et al. (2006)
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PrerequisiteRelation forSGM Competencies
K3
K4
K7
K8
K9
K10
K11
K12
K13
K15
K16
K20
S22
S23
S29
S30
S31
S32
S33
S34
K19
S25
System
Stakeholders
Adjacent
Systems
ContextModel
ProduceContext
Model
System Domain
and Environment
Ley et al. (2006)
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ThreeScenariosforSupportingWork-
integratedLearning 1.UpdatingtheUser Profile fromPerformedTasks
2.SuggestingResources forLearningfroma CompetencyGap
Analysis
3.ValidatingtheModels
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Scenario1: creating a competency profile
from performed tasks
Information on
Task
Performance

+ 5.1 5.2

-4.3 5.3 5.4
Diagnose
Competence
State

{ 13, 15}
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Scenario2: retrievingcontentfora
competencegap(1)
Ifthegoalisto perform
a task
suggestsequenceof
competenciesto learn

5.3 {20}

5.4 {16}

4.3 {20} or{16}, {3}
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Scenario2: retrievingcontentfora
competencegap(2) Invokinga learningtemplate

Competency{20}
Ability to produce
i*model

Connectedto knowledgetype
procedurallearning

Invokesa learningtemplatefor
“Learning by Example”
RetrievingContentfromexisting
documents

Learning Template looks for Material
Use
“Example”
and
“Procedure”

Domain Concepts:
i*model
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Scenario 3: Validating Models with the
“Leave One Out”Method Task performance information (successful vs. not successful) is
available for a subset t1
…t
n
of the tasks
Apply “leave one out”cross validation procedure
1.
take out one task (ti) [i=1…n] for which performance information is
available
2.
construct a competence performance structure from other n-1 tasks
3.
From this structure, predict whether t
i
is performed successfully
4.
Compare prediction to actual performance in ti
5.
Increase i=i+1 and go to step 1
Relate correct to incorrect predictions (e.g. by using )
τb
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Results for “leave one out”cross
validation procedure
τb
Ley et al. (2006)
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Summary: Why we suggest the
Competence Performance Approach
Providescloseconnectionof learningto taskperformancein the
workplace
Derivesdependencieson competencieswithoutneedto model
themexplicitly
Expertise isnotmodelledlinearly, buttherearea numberof
waysto learn
Formal modelallowsforvalidationin theprocessof modelling,
orin theprocessof operation
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ThankYou! Tobias Ley
Know-Center
Inffeldgasse 21a
8010 Graz
Austria
Phone: +43 316 873 9273
E-mail: tley@know-center.at
http://www.know-center.at
http://www.aposdle.org
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