Author: Gilbert Paquette Reuse freely – Just quote Meta-Knowledge Representation for Learning...

Author: Gilbert PaquetteReuse freely – Just quote

Meta-KnowledgeMeta-KnowledgeRepresentationRepresentation

for Learning Systems for Learning Systems

(Part 1-What)(Part 1-What) _________________________________

Gilbert Paquette

TICL Workshop

Montréal, April 14 2005


MISA 4.0 Method

Knowledge Modeling

210 Knowledge modeling principles212 Knowledge model214 Target competencies310 Learning units content 410 Learning instruments content 610 Knowledge and competency management

Instructional Modeling

220 Instructional principles222 Learning events network224 Learning units properties320 Instructional scenarios322 Learning activities properties420 Learning instruments properties620 Actors and group management

Materials Modeling

230 Media principles 330 Development infrastructure 430 Learning materials list 432 Learning materials models 434 Media elements 436 Source documents630 Learning system / resource management

Delivery Modeling

240 Delivery principles242 Cost-benefit analysis340 Delivery planning440 Delivery models442 Actors and user’s materials444 Tools and telecommunication446 Services and delivery locations540 Assessment planning640 Maintenance / quality management

Problem definition

100 Training system102 Training objectives

104 Target Learners106 Actual situation

108 Reference documents


MOT Graphic LanguageCONCEPTS PROCEDURES PRINCIPLES

C

S

P

I/P

R

I

LINKS


Desired Properties of a GraphicRepresentation Formalim

Simplicity and User FriendlinessSimplicity and User FriendlinessGeneralityGeneralityCompletenessCompletenessTranslated to machine (XML) formatTranslated to machine (XML) formatCommunicable between humansCommunicable between humansEasily InterpretableEasily InterpretableUsable at the meta-knowledge levelUsable at the meta-knowledge levelMaking explicit relationships between meta-Making explicit relationships between meta-knowledge and domain specific knowledgeknowledge and domain specific knowledge


Graphic Representation of a LD


Improve Knowledge Referencing in IMS-LD

IMS-LD is a progress in eLearning specifications

Assigning optional objectives and prerequisites is weak: IMS RDCEO specification (IMS 2002)

Consistency checking is not supported between levels nor between the content of learning activities and resources, and the actors’ competencyKnowledge in learning resources is not described Actor’s knowledge and competencies is only indirectly defined through educational objectives

Need for a qualitative structural representation of knowledge in activities, but also a quantitative one (for competency gaps processing)


A Graphic Ontology OWL Editor


A Graphic OWL Editor

S

ChemicalFertilizers

S

RiceProductionProcesses

I

I

I

RR Produce

RR Produce

CarbonDioxyde

S

Gases

S

R

Fertilizers

R

R

R

NitricOxyde

HasInputs

MethaneHas

Outputs

I

RR

RR

HasOutputs

HasInputs

SS

A CertainRice

Production

AgriculturalPractices

GreenhouseGases

THINGS


Referencing LDs with an Ontology


Structured Competencies

To say that somebody needs to acquire a certain knowledge is insufficient

What kind of generic skill + performance? A generic skills’ taxonomy based on different

viewpoints : instructional objectives, generic tasks/processes, meta-knowledge

Competency = Meta-process (skill) applied to a knowledge at a certain level of performance

Situate knowledge acquisition goals on a competency/performance scale


Skill/Performance ScaleSelf-manage (10)

Evaluate (9)

Synthesize (8)

Repair (7)

Analyze (6)

Apply (5)

Transpose (4)

Interpret (3)

Identify (2)

Memorize (1)

Pay attention (0)

Peter M

.

Book X

Video Y.

Multimedia Production Method

Skills

Performance Aware Familiarized Productive Expert


Explor@-II Delivery System


Competency Diagnosis Tool

Voir l’Évaluationdu formateur


Competency Equations

CC

C

C

Act 5

P

P

P

Activity 5.4

Activitiy 5.1

Activity 5.2

IP

IP

Productresource

Inputresource

Inputresource

Activity 5.3

R

TrainerLearner

IP

R

Components of a Function Components of a Function must reach competence must reach competence equilibrium . equilibrium . Ex: Learning resources (persons, Ex: Learning resources (persons, documents and tools) must enable documents and tools) must enable learners to progress from an entry learners to progress from an entry level to a target level required by the level to a target level required by the activity.activity.

Components of a Function Components of a Function must reach competence must reach competence equilibrium . equilibrium . Ex: Learning resources (persons, Ex: Learning resources (persons, documents and tools) must enable documents and tools) must enable learners to progress from an entry learners to progress from an entry level to a target level required by the level to a target level required by the activity.activity.

7.4

TC:7.4

TC: TC:7.4

EC:6.4

TC:7.4

TC:5.2

EC:5.2


Referencing Principles1. Tree organization of the knowledge referential:

allows competence inheritance from parent node to childrenreduce significantly the mechanisms of competence analysis and management.

2. Must be completed by relational logic to sustain more refined mechanism of conceptual matching.

3. Ontology referencing plus mastery levelsprevent coarse granulation of senseweak semantic management services.

4. Quantitative measures to weight ability on knowledge

level scale to be reasonably simple, manageable levels corresponding to clearly identify cognitive processes

5. Generic Skill’s Meta-process Representation as a Basis for Learning Scenarios


Meta-KnowledgeMeta-KnowledgeRepresentationRepresentation

for Learning Systemsfor Learning Systems

(Part 2 - How)(Part 2 - How) _________________________________

Gilbert Paquette

TICL Workshop

Montréal, April 14 2005


Fra

mew

ork

sGeneric Skills Taxonomy Layers

1 2 3

Active meta-knowledge

(Pitrat

Generic problems (KADS)

Cognitive objectives (Bloom)

Skills cycle (Romiszowski)

1. Pay Attention Attention

Rec

eive

2. Integrate 2.1 Identify

2.2 Memorize

Memorize Perceptual acuteness and discrimination

3. Instantiate / Specify

3.1 Illustrate

3.2 Discriminate

3.3 Explicitate

Knowledge Search and Storage

Understand Interpretation

4. Transpose/ Translate

Rep

rodu

ce

5. Apply 5.1 Use

5.2 Simulate

Knowledge Use, Expression

Apply

Procedure Recall Schema Recall

6. Analyze 6.1 Deduce

6.2 Classify

6.3 Predict

6.4 Diagnose

Prediction, Supervision, Classification, Diagnosis

Analyze Analysis

7. Repair Repair

Cre

ate

8. Synthesize 8.1 Induce

8.2 Plan

8.3 Model/ Construct

Knowledge Discovery

Planning, Design, Modeling

Synthesize

Synthesis

9. Evaluate Knowledge Acquisition

Evaluate Evaluation

Re-

inve

st

10. Self- manage

10.1 Influence

10.2 Self-control

Initiation, Continuation, Control


Classifying Meta-Processes

A generic skills’ taxonomy based on different viewpoints : instructional objectives, generic tasks/processes, meta-knowledge

Expandable taxonomy from general to specific

Ordering skills from simple to complex

Integrating domains of multiple intelligence: cognitive, affective, social, psycho-motor


A Generic Skills (Meta-process) Taxonomy

S

Identify S

Illustrate

Memorize

Utilize

S

S

S

Classify

Construct

Initiate/ Influence

Adapt/ control

S

S

S

S

Discriminate

Explicitate

SimulateDeduce

S

S

Predict

Diagnose

Induce

Plan

S

S

S SS

S

Exerce a skill

Receive

Reproduce

S

Create

Self- manage

S

S

1-Show awareness

S

9-Evaluate

S

4-Transpose

S

7-Repair

S

2-Internalize

S

3-Instantiate /Detail

S

5-Apply

S

6-Analyze

8-SynthesizeS

S

10-Self- manage

S

Generic skill Inputs Products

Simulate Process to simulate: inputs, products, sub-procedures, control principles

Trace of the procedure: set of facts obtained through the application of the procedure in a particular case

Construct Definition constraints to be satisfied such as target inputs, products or steps….

A model of the process: its inputs, products, sub-procedures each with their own inputs, products and control principles


Meta-Process and Domain Knowledge


Meta-Process, Skills and Attitudes


Building Competency Models


Competency ObjectivesId A- Law concept, regulations and standards of the profession Priority Entry Gap

A1 (6) Analyze the applicable texts of law to a situation, without help for simple and average complexity situations, with help in complex ones

1 (2) 4

A3 (3) Specify the applicables law regulation, autonomously in any case 2 (1) 2

A8 (5) Apply pertinent proofs and procedures, without help for simple and average complexity situations.

1 (2) 3

Id B- Communication with the client Priority Entry Gap

B1 (6) Analyze interactions with the client, without help in any communication situation.

2 (2) 4

B2 (9) Evaluate the quality of one’s capacity to listen to the client, without help in any communication situation

2 (1) 8

B4 (4) Transpose in one’s social and affectives interactions with the client, principles of communication and human behavior, sans aide, without help for average complexity situations.

2 (1) 3


Building Process-Based Scenarios


Lib

rary

of

Sce

nar

ios

Gen

eric

Pro

ble

ms

& T

asks


Methods of Identifying/ConstructingMeta-Knowledge

Individual and group, automated, semi-Individual and group, automated, semi-automated, interactive interviewsautomated, interactive interviews

Knowledge representation guided by Knowledge representation guided by competency gapscompetency gaps

Association of skills from a meta-process Association of skills from a meta-process taxonomy to main domain specific knowledgetaxonomy to main domain specific knowledge

Using the meta-process model to plan, deliver, Using the meta-process model to plan, deliver, analyze instructionanalyze instruction

Searching for knowledge/comptency equilibrium Searching for knowledge/comptency equilibrium (a concept to be explored)(a concept to be explored)

Author: Gilbert Paquette Reuse freely – Just quote Meta-Knowledge Representation for Learning...

Documents

Transcript of Author: Gilbert Paquette Reuse freely – Just quote Meta-Knowledge Representation for Learning...