Cognitive Psychology Theories for Knowledge Management Tobias Ley, - - PowerPoint PPT Presentation

aposdl aposdle – New w w ays ... ...

... to w

w ork, l lear arn and col nd collabor aborat ate! e!

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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 3

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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 4

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, …

Focussing on the Human Factor in Interacting with Computers

How to design interaction, interfaces and information? Usability, joy of use, learnability, fault tolerance, …

Focussing on Intelligent Applications

Designing computers to behave like humans More “intelligent” software applications and agents, adaptivity, …

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 5

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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 6

Cognitive Load and Learning

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 7

Die Struktur des Gedächtnisses

Cooper (1998)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 8

Sensorisches Gedächtnis

Ultrakurze Speicherungsdauer

Visuell (~ 0,5 sec) Auditiv (~ 3 sec)

Prä-attentive Verarbeitung

Wahrnehmungsorganisation nach Gestaltgesetzen

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 9

Langzeitgedächtnis

Inhalt:

Wissen und Fertigkeiten

Kapazität:

Prinzipiell unlimitiert

Prozesse

Aktivierung der Inhalte erfolgt über Anfragen des Arbeitsgedächtnisses

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 10

Arbeitsgedächtnis

Inhalte

Getrennte Systeme für auditiv-sprachliche Inhalte (phonological loop) und

visuell-bildliche Inhalte (visual sketchpad)

Kapazität

Begrenzte Zahl an Einheiten (<9) Chunking

Prozesse

Zentrale Rolle des AG für die Enkodierung Rolle der Aufmerksamkeit

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 11

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

TUG 707.009 Foundations of Knowledge Management 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 Sprachenlernen
• Vokabeln (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“

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 13

Cooper (1998)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 14

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)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 15

Zwei Arten von kognitiver Belastung (2)

Cooper (1998) leichter Stoff schwieriger Stoff & unpassendes Material schwieriger Stoff & passendes Material

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 16

Beispiel: Split Attention Effect

Sweller, Chandler, Tierney & Cooper (1990)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 17

Longterm Memory: Propositions and Associative Networks

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 18

Propositionalen Repräsentationen beim Textverstehen

{Lincoln; Präsident-von; USA} {Lincoln; befreien; Sklaven} {Krieg; bitter}

Anderson (2000)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 19

Der Aufbau von propositionalen Repräsentationen beim Textverstehen

Repräsentation ist elementaristisch Prozess ist additiv Verknüpfung von Elementen erfolgt im Arbeitsgedächtnis

direkt 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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 20

Spreading Activation Model des Abrufs aus dem Langzeitgedächtnis

Ai = Bi + ΣwjSji Sji = 2-log(Fanj) Untersuchungen zum Fächereffekt (“Fan Effect”)

Anderson & Lebiere (1998)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 21

Longterm Memory: Mental Models & Metaphors

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 22

Empirische Probleme mit Propositionalen Repräsentationen

Hans war auf dem Weg zur Schule … An der Kinokasse …

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 23

Der Aufbau vom mentalen Modellen beim Textverstehen

Holistische analoge Repräsentationsform

• i.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 Modells

• Übereinstimmung mit dem Text
• Plausibilität und Vollständigkeit

TUG 707.009 Foundations of Knowledge Management 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)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 25

Schnotz & Bannert, 2002

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 26

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 Prozess

• Zerlegen des Systems in

Teilkomponenten

• Bilden eines kausalen mentalen

Modells

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 27

Longterm Memory: Metaphors & Mental Models

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 28

Metaphern im Wissensmanagement

Implizites Wissen über “Wissen”

Wissen als Bibliothek Wissen als umkämpfter Schatz Wissen als Kanalisationssystem

Moser (2003)

aposdl aposdle – New w w ays ... ...

... to w

w ork, l lear arn and col nd collabor aborat ate! e!

Knowledge Space Theory

TUG 707.009 Foundations of Knowledge Management 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 learning

work-integrated assessment competency gap analysis validation

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 31

Knowledge Space Theory – The Fundamentals

Doignon and Falmagne‘s (1999) intention: „to built an efficient machine 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 combinatorics with no attempt for obtaining a numerical representation Starting Point is a possibly large but essentially discrete set of units of knowledge

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 32

Looking at the Person Knowledge State of a Person determined from the performance in the tasks

A knowledge domain can be viewed in two respects

Looking at the Tasks Solution Dependencies within the tasks of a domain

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 33

Tasks can be structured according to a Prerequisite Relation

Q Domain of knowledge: Collection of all tasks in the domain SR Prerequisite Relation capturing solution dependencies in the tasks in Q SR is reflexive and transitive

c b a

Q q r, q r ∈ p

c a p c b p

b a p

Example

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 34

A Knowledge State describes the knowledge of a person

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

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: The Collection of all Knowledge States If K is closed under union, the knowledge structure is called Knowledge Space

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 35

Knowledge Space and Prerequisite Relation: Two sides of the same coin

(Q,K) K B

∈

a b c d e b a b a a b c d e a

(Q, )

p

QXQ ⊆ p

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 36

Using Knowledge Spaces in Adaptive Tutoring

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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 37

Using Knowledge Spaces in Adaptive Tutoring

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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 38

Knowledge in a domain is modelled as a set of possible knowledge states A Knowledge Space can be validated by comparing it to the empirically

• bserved answer patterns

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

What Knowledge Space Theory can do

(Korossy, 1997)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 39

It is only a descriptive model without consideration for the underlying cognitive processes Therefore a transfer of the diagosis to other tasks is not possible Gives only a simple recommendations for learning interventions

What Knowledge Space Theory can not do

(Korossy, 1997)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 40

Competency Based Knowledge Space Theory

Competence Performance Approach (Korossy, 1993) Adding a theoretical component underlying the observable solution behavior Knowledge is modelled as competence and performance Competencies: Knowledge and skills needed to produce performance Competence model is derived from general or domain specific learning theories about the development of knowledge and skills

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 41

The Competence Performance Approach

) , ( P A

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

℘ →

Competence Space

) , ( K

ε

ε

ε ∈

ε

ε ε ε ε ε ε ε ε ε ε ε ε ε ε ε

K

K ∈

ε

TUG 707.009 Foundations of Knowledge Management 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 learning

work-integrated assessment competency gap analysis validation

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 43

Work-integrated Learning with APOSDLE

Real Time Real Place Real Content Real Backend Systems

www.aposdle.org

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 44

Modelling for 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)

TUG 707.009 Foundations of Knowledge Management 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 the basis for 1. Competence Performance Structure 2. Prerequisite Relation on the set of competencies Ley et al. (2006)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 47

Competence Performance Structure (Example)

Ley et al. (2006)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 48

Prerequisite Relation for SGM 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 Context Model Produce Context Model System Domain and Environment

Ley et al. (2006)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 49

Three Scenarios for Supporting Work- integrated Learning

• 1. Updating the User Profile from Performed Tasks
• 2. Suggesting Resources for Learning from a Competency Gap

Analysis

• 3. Validating the Models

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 50

Scenario 1: 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}

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 51

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

If the goal is to perform a task suggest sequence of competencies to learn

5.3 {20} 5.4 {16} 4.3 {20} or {16}, {3}

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 52

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

Invoking a learning template

• Competency {20} Ability to produce

i*model

• Connected to knowledge type

procedural learning

• Invokes a learning template for

“Learning by Example”

Retrieving Content from existing documents

• Learning Template looks for Material

Use “Example” and “Procedure”

• Domain Concepts: i*model

TUG 707.009 Foundations of Knowledge Management 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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 54

Results for “leave one out” cross validation procedure

τb

Ley et al. (2006)

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 55

Summary: Why we suggest the Competence Performance Approach

Provides close connection of learning to task performance in the workplace Derives dependencies on competencies without need to model them 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,

• r in the process of operation

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 56

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 57

Thank You!

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

TUG 707.009 Foundations of Knowledge Management 02 Dec 2008 / 58

References

Albert, 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 Learning to 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-Blättler & 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.