cognitive psychology theories for knowledge...

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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


02 Dec 2008 / 3

Cognitive Psychology: What it isPsychology: The study of Human Behavior

Explanation and Prediction of Human Mental Processes und Behavior

Validation of Theories and Models

AreasCognition, Emotions

Social and Group Interactions

Individual Differences and Personality

Organizational & Work, Educational, Clinical, Traffic, Forensic

CognitionHigh 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.

MethodsClinical Diagnostic Findings, Expert-Novice Contrasts, Reaction Time Experiments, Computational Models, Brain Imaging Techniques

http://www.lhsc.on.ca/programs/msclinic/define/c.htm


02 Dec 2008 / 4

Cognitive Psychology: Why it is relevant for Knowledge Management

Changing Human Behavior in Organizational SettingsHow to design organizational settings to change human behavior?

Effectiveness, efficiency, health, motivation, satisfaction, …

Focussing on the Human Factor in Interacting with ComputersHow to design interaction, interfaces and information?

Usability, joy of use, learnability, fault tolerance, …

Focussing on Intelligent ApplicationsDesigning 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 desGedächtnisses

Cooper (1998)


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Sensorisches Gedächtnis

Ultrakurze SpeicherungsdauerVisuell (~ 0,5 sec)

Auditiv (~ 3 sec)

Prä-attentive VerarbeitungWahrnehmungsorganisation nach Gestaltgesetzen


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Langzeitgedächtnis

Inhalt: Wissen und Fertigkeiten

Kapazität: Prinzipiell unlimitiert

ProzesseAktivierung der Inhalte erfolgt über Anfragen des Arbeitsgedächtnisses


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Arbeitsgedächtnis

InhalteGetrennte Systeme für auditiv-sprachliche Inhalte (phonological loop) und visuell-bildliche Inhalte (visual sketchpad)

KapazitätBegrenzte Zahl an Einheiten (<9)

Chunking

ProzesseZentrale Rolle des AG für die Enkodierung

Rolle der Aufmerksamkeit


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Cognitive Load Theory –Theorie der kognitiven Belastung

Was ist kognitive Belastung?Maß an mentaler Aktivität, die das Arbeitsgedächtnis in einer bestimmten Zeiteinheit belastet

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

Cognitive Load ist nicht gleich Aufgabenschwierigkeit

Beispiel: Merken von Zahlenreihen


02 Dec 2008 / 12

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 SprachenlernenVokabeln (low item interactivity)

Grammatik (high item interactivity)

Beispiel Verwandtschaften (vgl. Cooper, 1998)True or false? „My father‘s brother‘s grandfather is my grandfatrher‘s brother‘s son“


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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 instruktionalen Design und vom verwendeten Lernmaterial

Cooper (1998)


02 Dec 2008 / 15

Zwei Arten von kognitiver Belastung (2)

Cooper (1998)

leichter Stoff

schwieriger Stoff& unpassendes

Material

schwieriger Stoff& passendes

Material


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Beispiel: Split Attention Effect

Sweller, Chandler, Tierney & Cooper (1990)


02 Dec 2008 / 17

Longterm Memory: Propositions and Associative Networks


02 Dec 2008 / 18

Propositionalen Repräsentationen beim Textverstehen

{Lincoln; Präsident-von; USA}

{Lincoln; befreien; Sklaven}

{Krieg; bitter}

Anderson (2000)


02 Dec 2008 / 19

Der Aufbau von propositionalenRepräsentationen beim Textverstehen

Repräsentation ist elementaristisch

Prozess ist additiv

Verknüpfung von Elementen erfolgt im Arbeitsgedächtnisdirekt wenn beide Propositionen im AG repräsentiert sind

schwieriger wenn eine Proposition aus dem LZG abgerufen werden muss

am schwierigsten wenn eine „Lücke“ entsteht und eine Inferenz (neue Proposition) gebildet werden muss


02 Dec 2008 / 20

Spreading Activation Model des Abrufsaus dem Langzeitgedächtnis

Ai = Bi + ΣwjSji Sji = 2-log(Fanj)

Untersuchungen zum Fächereffekt (“Fan Effect”)

Anderson & Lebiere (1998)


02 Dec 2008 / 21

Longterm Memory: Mental Models & Metaphors


02 Dec 2008 / 22

Empirische Probleme mit Propositionalen Repräsentationen

Hans war auf dem Weg zur Schule …

An der Kinokasse …


02 Dec 2008 / 23

Der Aufbau vom mentalen Modellen beim Textverstehen

Holistische analoge Repräsentationsformi.ggs. zu Propositionen als digitale Repräsentation

Aktivierung von Vorwissen

Elaboration von „Szenarien“Skripts, Schemata, Frames

Top-Down Verarbeitung„Leerstellen“ als Fragen an den Text

Informationssuche oder Inferenz

Fortlaufende Evaluation des Mentalen ModellsÜbereinstimmung mit dem Text

Plausibilität und Vollständigkeit


02 Dec 2008 / 24

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: Mentale Repräsentation von technischen Systemen

Mayer, Mathias, & Wetzell (2003)

Mentales Modell des Systems erlaubt

Bilden von Inferenzen

Interne mentale Simulation von Abläufen

Beantwortung von Transferaufgaben

Lernen als 2-stufiger ProzessZerlegen des Systems in Teilkomponenten

Bilden eines kausalen mentalenModells


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Longterm Memory: Metaphors & Mental Models


02 Dec 2008 / 28

Metaphern imWissensmanagement

Implizites Wissen über “Wissen”Wissen als Bibliothek

Wissen als umkämpfter Schatz

Wissen als Kanalisationssystem

Moser (2003)

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Knowledge Space Theory


02 Dec 2008 / 30

Overview

Knowledge Space Theory: the fundamentals

A competency based extension: the Competence Performance Approach

Applying Knowledge Space Theory in modelling for work-integrated learning

Three scenarios for supporting work integrated learningwork-integrated assessment

competency gap analysis

validation


02 Dec 2008 / 31

Knowledge Space Theory –The Fundamentals

Doignon and Falmagne‘s (1999) intention: „to built an efficientmachine for the asessment of 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 combinatoricswith 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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Looking at the Person

Knowledge State of a Person determined fromthe performance in thetasks

A knowledge domain can be viewed in two respectsLooking at the Tasks

Solution Dependencieswithin the tasks of a domain


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Tasks can be structured according to a Prerequisite RelationQ Domain of knowledge: Collection of

all tasks in the domain

SR Prerequisite Relation capturingsolution dependencies in the tasksin Q

SR is reflexive and transitive c

ba

Qqr, qr ∈p

ca p cb p ba p

Example


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A Knowledge State describes theknowledge of a person

Example

Q={a,b,c}

K={{},{a},{b},{a,b},{a,b,c}}

c

ba

K∈K K∈∅ Q,

Q Domain of knowledge: Collection of all tasks in the domain

K Knowledge State: A subset of Q

K Knowledge Structure: TheCollection of all Knowledge States

If K is closed under union, theknowledge structure is calledKnowledge Space


02 Dec 2008 / 35

Knowledge Space and PrerequisiteRelation: Two sides of the same coin

(Q,K)

K B∈

a

b

c d

e

ba baa

b c d

e

a

(Q, )p

QXQ⊆p


02 Dec 2008 / 36

Using Knowledge Spaces in Adaptive Tutoring

Falmagne et al., 2004; http://www.aleks.com


02 Dec 2008 / 37

Using Knowledge Spaces in Adaptive Tutoring

Falmagne et al., 2004; http://www.aleks.com


02 Dec 2008 / 38

Knowledge in a domain is modelled as a set of possible knowledgestates

A Knowledge Space can be validated by comparing it to the empiricallyobserved answer patterns

A valid Knowledge Space can be used for individualized and adaptive knowledge diagnosis

What Knowledge Space Theorycan do

(Korossy, 1997)


02 Dec 2008 / 39

It is only a descriptive model without consideration for theunderlying cognitive processes

Therefore a transfer of the diagosis to other tasks is notpossible

Gives only a simple recommendations for learning interventions

What Knowledge Space Theorycan not do

(Korossy, 1997)


02 Dec 2008 / 40

Competency BasedKnowledge Space Theory

Competence Performance Approach (Korossy, 1993)

Adding a theoretical component underlying the observablesolution behavior

Knowledge is modelled as competence and performance

Competencies: Knowledge and skills needed to produceperformance

Competence model is derived from general or domain specificlearning theories about the development of knowledge and skills


02 Dec 2008 / 41

The Competence Performance Approach

),( PA

A∈x A

P∈Z

Performance Space

x

x x

x

xx

x

x

x

xx

x

x

x

x

xx

)( :k KA ℘→

)( :p AK ℘→

Competence Space ),( Kε

εε ∈ εε

εεε ε

εεε

εε

εε ε

ε

εKK ∈

ε


02 Dec 2008 / 42

Overview

Knowledge Space Theory: the fundamentals

A competency based extension: the Competence Performance Approach

Applying Knowledge Space Theory in modelling for work-integrated learning

Three scenarios for supporting work integrated learningwork-integrated assessment

competency gap analysis

validation


02 Dec 2008 / 43

Work-integrated Learning with APOSDLE

Real Time

Real Place

Real Content

Real Backend Systems

www.aposdle.org


02 Dec 2008 / 44

Modelling for an Adaptive Technology Enhanced Learning Environment

Three Models are needed to support adaptivityKnowledge 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)


02 Dec 2008 / 45

RESCUE: The Learning Domain

“Requirements Engineering” as the learning domain for the first prototype

RESCUE - Requirements Engineering with Scenarios in User-Centered Environments (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 thebasis for1. Competence Performance Structure 2. Prerequisite Relation on the set of

competencies

Ley et al. (2006)


02 Dec 2008 / 47

Competence Performance Structure(Example)

Ley et al. (2006)


02 Dec 2008 / 48

Prerequisite Relation for SGM Competencies

K3K4

K7K8

K9

K10

K11

K12

K13 K15

K16

K20

S22S23 S29S30

S31

S32

S33

S34

K19

S25

SystemStakeholders

AdjacentSystems

Context Model

Produce ContextModel

System Domain and Environment

Ley et al. (2006)


02 Dec 2008 / 49

Three Scenarios for Supporting Work-integrated Learning1. Updating the User Profile from Performed Tasks

2. Suggesting Resources for Learning from a Competency Gap Analysis

3. Validating the Models


02 Dec 2008 / 50

Scenario 1: creating a competency profile from performed tasks

Information on TaskPerformance

+ 5.1 5.2

- 4.3 5.3 5.4

DiagnoseCompetenceState

{ 13, 15}


02 Dec 2008 / 51

Scenario 2: retrieving content for a competence gap (1)

If the goal is to performa task

suggest sequence of competencies to learn

5.3 {20}

5.4 {16}

4.3 {20} or {16}, {3}


02 Dec 2008 / 52

Scenario 2: retrieving content for a competence gap (2)

Invoking a learning templateCompetency {20} Ability to producei*model

Connected to knowledge typeprocedural learning

Invokes a learning template for“Learning by Example”

Retrieving Content from existingdocuments

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

Domain Concepts: i*model


02 Dec 2008 / 53

Scenario 3: Validating Models with the “Leave One Out” Method

Task performance information (successful vs. not successful) is available for a subset t1 … tn 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 ti 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


02 Dec 2008 / 54

Results for “leave one out” cross validation procedure

τb

Ley et al. (2006)


02 Dec 2008 / 55

Summary: Why we suggest the Competence Performance Approach

Provides close connection of learning to task performance in theworkplace

Derives dependencies on competencies without need to modelthem explicitly

Expertise is not modelled linearly, but there are a number of ways to learn

Formal model allows for validation in the process of modelling, or in the process of operation


02 Dec 2008 / 56


02 Dec 2008 / 57

Thank You!

Tobias Ley

Know-CenterInffeldgasse 21a8010 GrazAustriaPhone: +43 316 873 9273E-mail: [email protected]://www.know-center.at

http://www.aposdle.org


02 Dec 2008 / 58

ReferencesAlbert, D., Hockemeyer, C., & Wesiak, G. (2002). Current Trends in eLearning based on Knowledge Space Theory and Cognitive Psychology. Psychologische Beiträge,

4(44), 478-494.

Anderson, J. R. (2000). Cognitive Psychology and its Implications. New York: Worth Publishing.

Anderson, J.R. and Lebiere, C. (1998). The Atomic Components of Thought, Lawrence Erlbaum Associates

Anderson, L.W., & Krathwohl (Eds.). (2001). A Taxonomy for Learning,Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives. New York: Longman.

Conlan, O., Hockemeyer, C., Wade, V., & Albert, D. (2002). Metadata driven approaches to facilitate adaptivity in personalized eLearning systems. The Journal of Information and Systems in Education, 1, 38-44.

Cooper, G. (1998). Research into Cognitive Load Theory and Instructional Design at UNSW.University of New South Wales, Australia. http://education.arts.unsw.edu.au/CLT_NET_Aug_97.HTML

Doignon, J.-P. & Falmagne, J-C. (1999). Knowledge Spaces. Heidelberg: Springer.

Doignon, J.-P. & Falmagne, J-C. (1985). Spaces for the assessment of knowledge. International Journal of Man-Machine Studies, 23, 175-196.

Falmagne, J. C., Cosyn, E., Doignon, J., & Thiéry, N. (2004). The Assessment of Knowledge in Theory and Practice. Unpublished Manusript. Irvine/CA: ALEKS Corp., last accessed on 30 May 2007 at http://www.aleks.com/about_aleks/Science_Behind_ALEKS.pdf.

Hockemeyer, C., Conlan, O., Wade, V., & Albert, D. (2003). Applying Competence Prerequisite Structures for eLearning and Skill Management. Journal of Universal Computer Science, 9(12), 1428-1436.

Korossy, K. (1993). Modellierung von Wissen als Kompetenz und Performanz. Eine Erweiterung der Wissensstruktur-Theorie von Doignon & Falmagne. Universität Heidelberg: Dissertation.

Korossy, K. (1997). Extending the theory of knowledge spaces: a competence-performance approach. Zeitschrift für Psychologie, 205, 53-82.

Korossy, K.(1999). Qualitativ-strukturelle Wissensmodellierung in der elementaren Teilbarkeitslehre. Zeitschrift für Experimentelle Psychologie, 46 (1), 28-52.

Ley, T. & Albert, D. (2003a). Kompetenzmanagement als formalisierbare Abbildung von Wissen und Handeln für das Personalwesen. Wirtschaftspsychologie, 5 (3), 86-93.

Ley, T. & Albert, D. (2003b). Identifying employee competencies in dynamic work domains: Methodological considerations and a case study. Journal of Universal Computer Science, 9 (12), 1500-1518.

Ley, T., Kump, B., Lindstaedt, S. N., Albert, D., Maiden, N. A. M., & Jones, S. V. (2006). Competence and Performance in Requirements Engineering: Bringing Learningto the Workplace. Proceedings of the Joint Workshop on Professional Learning, Competence Development and Knowledge Management, October 2006, 42-52, Crete, Greece (pp. 42-52). Lodon: Open University.

Maiden, N.A.M., & Jones, S.V. (2004a). The RESCUE Requirements Engineering Process – An Integrated User-centered Requirements Engineering Process, Version 4.1. Report, Centre for HCI Design, The City University, London.

Moser, K. S. (2003). Mentale Modelle und ihre Bedeutung: kognitionspsychologische Grundlagen des (Miss)Verstehens. In U. Ganz-BlÃ¤ttler & P. Michel (Eds.), Sinnbildlich schief: Missgriffe bei Symbolgenese und Symbolgebrauch (Schriften zur Symbolforschung, Vol. 13). Bern: Peter Lang (pp. 181-205).

Schnotz, Wolfgang; Bannert, Maria (2002). Construction and interference in learning from multiple representation, Learning and Instruction, 13, 141–156.

Sweller, J., Chandler, P., Tierney, P. and Cooper, M. (1990). Cognitive load and selective attention as factors in the structuring of technical material. Journal of Experimental Psychology: General, 119, 176-192.

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