Cognitive Psychology Theories for Knowledge Management

Transkript

1 Cognitive Psychology Theories for Knowledge Management Tobias Ley, Know-Center aposdle New ways to work, learn and collaborate!

2 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

3 02 Dec 2008 / 3 Cognitive Psychology: What it is Psychology: The study of Human Behavior Areas Explanation and Prediction of Human Mental Processes und Behavior Validation of Theories and Models 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, problemsolving, and self-monitoring. Methods Clinical Diagnostic Findings, Expert-Novice Contrasts, Reaction Time Experiments, Computational Models, Brain Imaging Techniques

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, 02 Dec 2008 / 4 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,

5 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

6 02 Dec 2008 / 6 Cognitive Load and Learning

7 Die Struktur des Gedächtnisses 02 Dec 2008 / 7 Cooper (1998)

8 02 Dec 2008 / 8 Sensorisches Gedächtnis Ultrakurze Speicherungsdauer Visuell (~ 0,5 sec) Auditiv (~ 3 sec) Prä-attentive Verarbeitung Wahrnehmungsorganisation nach Gestaltgesetzen

9 02 Dec 2008 / 9 Langzeitgedächtnis Inhalt: Wissen und Fertigkeiten Kapazität: Prinzipiell unlimitiert Prozesse Aktivierung der Inhalte erfolgt über Anfragen des Arbeitsgedächtnisses

10 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

11 Cognitive Load Theory Theorie der kognitiven Belastung 02 Dec 2008 / 11 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

12 Die Rolle der kognitiven Belastung beim Lernen Warum ist bestimmtes Material schwer zu erlernen? 1. Anzahl an zu lernenden Elementen ist hoch 02 Dec 2008 / 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

13 02 Dec 2008 / 13 Cooper (1998)

14 Zwei Arten von kognitiver Belastung (1) 02 Dec 2008 / 14 Aufgaben-inhärent ( intrinsic ) Nur abhängig von der Schwierigkeit des zu lernenden Stoffs Zahl und Zusammenhang der Einheiten Aufgaben-extern ( extraneous ) Cooper (1998) Abhängig vom instruktionalen Design und vom verwendeten Lernmaterial

15 Zwei Arten von kognitiver Belastung (2) 02 Dec 2008 / 15 leichter Stoff schwieriger Stoff & unpassendes Material schwieriger Stoff & passendes Material Cooper (1998)

16 02 Dec 2008 / 16 Beispiel: Split Attention Effect Sweller, Chandler, Tierney & Cooper (1990)

17 Longterm Memory: Propositions and Associative Networks 02 Dec 2008 / 17

18 Propositionalen Repräsentationen beim Textverstehen 02 Dec 2008 / 18 {Lincoln; Präsident-von; USA} {Lincoln; befreien; Sklaven} {Krieg; bitter} Anderson (2000)

19 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

20 02 Dec 2008 / 20 Spreading Activation Model des Abrufs aus dem Langzeitgedächtnis A i = B i + Σw j S ji S ji = 2-log(Fan j ) Untersuchungen zum Fächereffekt ( Fan Effect ) Anderson & Lebiere (1998)

21 Longterm Memory: Mental Models & Metaphors 02 Dec 2008 / 21

22 Empirische Probleme mit Propositionalen Repräsentationen Hans war auf dem Weg zur Schule An der Kinokasse 02 Dec 2008 / 22

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 02 Dec 2008 / 23

24 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)

25 02 Dec 2008 / 25 Schnotz & Bannert, 2002

26 02 Dec 2008 / 26 Beispiel: Mentale Repräsentation von technischen Systemen 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 Mayer, Mathias, & Wetzell (2003)

27 Longterm Memory: Metaphors & Mental Models 02 Dec 2008 / 27

28 Metaphern im Wissensmanagement 02 Dec 2008 / 28 Implizites Wissen über Wissen Wissen als Bibliothek Wissen als umkämpfter Schatz Wissen als Kanalisationssystem Moser (2003)

29 Knowledge Space Theory aposdle New ways to work, learn and collaborate!

30 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 workintegrated learning Three scenarios for supporting work integrated learning work-integrated assessment competency gap analysis validation

31 Knowledge Space Theory The Fundamentals 02 Dec 2008 / 31 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

32 02 Dec 2008 / 32 A knowledge domain can be viewed in two respects Looking at the Tasks Looking at the Person Solution Dependencies within the tasks of a domain Knowledge State of a Person determined from the performance in the tasks

33 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 r p q r,q Q SR is reflexive and transitive Example a c b a p c b p c a p b

34 02 Dec 2008 / 34 A Knowledge State describes the knowledge of a person 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 K K, Q K If K is closed under union, the knowledge structure is called Knowledge Space Example c Q={a,b,c} a b K={{},{a},{b},{a,b},{a,b,c}}

35 Knowledge Space and Prerequisite Relation: Two sides of the same coin 02 Dec 2008 / 35 (Q,K) K B e e (Q, p ) c d a b c d b a a b a b a p QXQ

36 Using Knowledge Spaces in Adaptive Tutoring 02 Dec 2008 / 36 Falmagne et al., 2004;

37 Using Knowledge Spaces in Adaptive Tutoring 02 Dec 2008 / 37 Falmagne et al., 2004;

38 02 Dec 2008 / 38 What Knowledge Space Theory can do 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 observed answer patterns A valid Knowledge Space can be used for individualized and adaptive knowledge diagnosis (Korossy, 1997)

39 What Knowledge Space Theory can not do 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 (Korossy, 1997)

40 Competency Based Knowledge Space Theory 02 Dec 2008 / 40 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

41 02 Dec 2008 / 41 The Competence Performance Approach ), ( P A A x A Z P 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

42 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 workintegrated learning Three scenarios for supporting work integrated learning work-integrated assessment competency gap analysis validation

43 02 Dec 2008 / 43 Work-integrated Learning with APOSDLE Real Time Real Place Real Content Real Backend Systems

44 02 Dec 2008 / 44 Modelling for an Adaptive Technology Enhanced Learning Environment Three Models are needed to support adaptivity Knowledge Base Surmise Relation on the set of competencies Student Model Teaching Model Deriving a Competency State from tasks performed in the past Using competency as a learning goal to devise educational interventions (learning events) Albert et al., 2002

45 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

46 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 Task-Competency Assignment Competencies Tasks 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} 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) Task Competency Assignment provides the basis for 1. Competence Performance Structure 2. Prerequisite Relation on the set of competencies 15 Knowledge about the Strategic Rationale Model (SR-Model) 16 Knowledge of validating the SR Model 20 Ability to produce an i* Model Ley et al. (2006)

47 Competence Performance Structure (Example) 02 Dec 2008 / 47 Ley et al. (2006)

48 02 Dec 2008 / 48 Prerequisite Relation for SGM Competencies System Domain and Environment K8 K7 S32 S30 S22 S23 S29 Produce Context Model Context Model K19 K12 K10 K4 K3 K16 S31 S34 Adjacent Systems K11 K13 K15 K20 S33 S25 System Stakeholders K9 Ley et al. (2006)

49 02 Dec 2008 / 49 Three Scenarios for Supporting Workintegrated Learning 1. Updating the User Profile from Performed Tasks 2. Suggesting Resources for Learning from a Competency Gap Analysis 3. Validating the Models

50 Scenario 1: creating a competency profile from performed tasks Information on Task Performance Diagnose Competence State { 13, 15} 02 Dec 2008 / 50

51 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}

52 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

53 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 t 1 t n of the tasks Apply leave one out cross validation procedure 1. take out one task (t i ) [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 t i 5. Increase i=i+1 and go to step 1 Relate correct to incorrect predictions (e.g. by using τ b )

54 Results for leave one out cross validation procedure 02 Dec 2008 / 54 τ b Ley et al. (2006)

55 Summary: Why we suggest the Competence Performance Approach 02 Dec 2008 / 55 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, or in the process of operation

56 02 Dec 2008 / 56

57 02 Dec 2008 / 57 Thank You! Tobias Ley Know-Center Inffeldgasse 21a 8010 Graz Austria Phone: tley@know-center.at

58 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), 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, Cooper, G. (1998). Research into Cognitive Load Theory and Instructional Design at UNSW.University of New South Wales, Australia. 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, 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 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), 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, Korossy, K.(1999). Qualitativ-strukturelle Wissensmodellierung in der elementaren Teilbarkeitslehre. Zeitschrift für Experimentelle Psychologie, 46 (1), Ley, T. & Albert, D. (2003a). Kompetenzmanagement als formalisierbare Abbildung von Wissen und Handeln für das Personalwesen. Wirtschaftspsychologie, 5 (3), 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), 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 ). 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-Blà ttler & P. Michel (Eds.), Sinnbildlich schief: Missgriffe bei Symbolgenese und Symbolgebrauch (Schriften zur Symbolforschung, Vol. 13). Bern: Peter Lang (pp ). Schnotz, Wolfgang; Bannert, Maria (2002). Construction and interference in learning from multiple representation, Learning and Instruction, 13, 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,