ICALT06, Kerkhade, The Netherlands, July 57, 2006 Knowledge, Competency and Educational Modeling for

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ICALT-06, Kerkhade, The Netherlands,July 5-7,
2006 Knowledge, Competency and Educational
Modeling for Lifelong Learning Gilbert
PaquetteLORNET and CICE DirectorLICEF
Research Center, Télé-universitéwww.licef.teluq.u
quebec.ca/gp

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Plan

• The Global Context Knowledge Management and L3
• Knowledge Dissemination Processes and Tools
• Knowledge Extraction Processes and Tools
• Whats in a Competency?
• Conclusion

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Bookmarks
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1- The Global Context

• L3 Lifelong Learning
• Knowledge Management Enhancing Human Competency
• Access to huge ressource/knowledge repositories
• Personalization context
• Towards the (more) Intelligent Web

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Challenge of the Knowledge Society

• LifeLong Learning Do we have flexible, adaptive
  learning systems, inside and outside public
  education system, before, after and during work?
• Huge demand for Web-based resources for work and
  learning Is the Learning Object Paradigm
  useful, usable now, at what condition?
• Knowledge Management in organizations means
  knowledge extraction and acquisition (learning)
  Do we have Educational Modeling Languages to
  support it ?
• The Web itself is becoming insufficient to
  support intelligent information requests. How
  could a new semantic web generation help solve
  the information overload and learning problems?

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Knowledge ManagementEnhancing Human Competency

• More than  glorified  document management
• At the knowledge level
• The goal knowledge and competency sharing in an
  organization
• Competency implies higher level knowledge
• Central role for competency-based Instructional
  engineering.

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Huge Ressource / Knowledge Repositories on the
Web(Not that available)
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Towards a More Intelligent Web

• First wave
• STATIC TEXT / DATA EXCHANGE
• Second wave
• DYNAMIC / MULTIMEDIA
• Third wave
• SEMANTIC
• PROGRAMMABLE
• PERSONALIZABLE

Semantic annotations to resources enable
intelligent processing search, matching,
guiding,
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LORNET Project - TELOS

• Pan-canadian Research Network
• 6 Universities, 4 Research Chairs, 15 entreprises
• 5 years, 7.5 M , 120 investigators
• Develop tools and
• methods to build
• personalized access
• to learning object
• repositories
• through the Semantic Web
• for learning and Knowledge
• Management

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Personalizing Learning(TELOS Main Use Cases)
ltTechnologistgt
ltEngineergt
ltLearnergt
ltDesignergt
ltAdministratorgt
Support Generation Operations
Use Core to Produce LKMS (Platforms)
Use LKMA to Produce LKMP (Products)
Use LKMS to Produce LKMA (Environments)
Extend TELOS Core
ltFacilitatorgt
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TELOS High-Level View

• Distributed
• Service oriented
• Ontology-Driven
• Component Aggregation

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2- Knowledge Extraction Processes and Tools

• The MOT Modeling Language
• Typology of Knowledge Models
• Methods
• Co-Modeling,
• Knowledge Engineering,
• Loose and Formal Modeling
• MOT Specializations
• Multi-actor workflows
• Ontologies

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MOT Modeling Language
L I N K S
CONCEPTS
PROCEDURES
PRINCIPLES
C
S
P
I/P
R
I
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A Simple Process Model
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An OWL-DL Model
THINGS
S
S
S
Agricultural
Has
Gases
Produce
Fertilizers
R
R
R
R
Practices
Inputs
S
S
R
Has
C
R
Outputs
Greenhouse
Rice
Gases
Chemical
Production
Fertilizers
Processes
I
I
I
Carbon
Has
Nitric
I
Produce
R
R
R
Dioxyde
Inputs
Oxyde
R
Traditional
Rice
Production
R
Has
Methane
R
Outputs
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3- Knowledge Dissemination Processes and Tools

• Resources repositories content, tools, process
  (LD)
• MOTLD a graphic IMS-LD Modeling Editor
• IDLD a Repository of Learning Designs
• A Reuse and Expand Process
• LD Primitive Template an Educational Genome
• Semantic Annotation

Information
Instructional Engineering
Shared Knowledge and Competencies
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Learning Design as Composed Objects
Basic Resources
Operations Scenarios Processes
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An IMS-LD Model
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An OWL-DL Model
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Semantic Referencing of Learning Objects
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Semantic Referential for LDs
Design
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A Library of LD Resourcewww.idld.org
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Reusability Process

• Modeling a Course and Generalizing to a Pattern
  (Instructional Strategy Level)
• Decomposing into Primitive LD or Direct Creation
  (Instructional Tactic Level)
• Aggregating a Course Pattern from primitive LD
  (New Strategy)
• Specification of Course Pattern to one or more
  Course Examplar

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4- Whats in a Competency?

• Competency Use
• Human resource management
• Guide knowledge modeling
• Define dcenarios, learning designs, courses
• Guide tutoring and learning resource
  self-management
• Select information resources, etc.
• Many projects, interesting process, little use
• Structured Competency
• Examples
• Research on Competency Equilibrium

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Structured Competencies

• To say that somebody needs to acquire a certain
  knowledge is insufficient. What should he be able
  to do with it ?
• Competency referentials address that problem by
  using natural language statements involving
  actions on knowledge
• Competency statements are most of the time
  ambiguous and difficult to use
• Need to structure competencies knowledge,
  skills/attitude, performance/context of use.
• A generic skills taxonomy combining viewpoints
  instructional objectives, generic
  tasks/processes, meta-knowledge

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Comparing Generic Skills Taxonomies
Active meta
-
Generic
Cognitive
Skills cycle

knowledge

problems

objectives

(Romiszowski)

(Pitrat

(KADS)

(Bloom)



Attention



Memorize

Perceptual
acuteness and
discriminatio
n

Knowledge
S
earch

Under
stand

Interpretation

and
S
torage



P
rocedure
Recall

Schema
Recall

Knowledge
U
se,

Apply

pression

Prediction,
Analyze

Analysis

Supervision,

Classification,

Diagnosis



Repair



Synthesis

Planning, Design,
Synthesize

Knowledge


discovery
Modeling
Knowledge

Evaluate

Evaluation

cquisition



Initiation,
Continuation,
Control

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A Generic Skills Taxonomy
S
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Performance / Application Context
PERFORMANCE LEVELS
CRITERIA
Frequency
Scope
Autonomy
Complexity
Context
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Backbone of an LD Repository
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Competency-based Semantic Annotation
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Competency Association to Activities and
Resources in Explor_at_-II
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Competency Gap Analysis
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Reduce Quitting Risk
L
S
T
S
Competency, Affective, Social, metacognitive data
(from tools)
R
R
R
Group Indicators (Ex actual Competency vs
target)
Trace each learner and tutor evaluation
Calculate
D
Model of the envirnt, the task (LD) the
domain ontology and entry/target competency
Individual / group diagnosis
Compare Diagnose
Build the LD and the envirnt
R
S
R
LEGEND
Learner
L
Communicate Diagnosis to A, T, D and S
Diagnosis Interface
Tutor
T
D
Designer
System
S
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Constructing a Professional Program (Bar
Professional School)
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Generic Skill a Blueprint for a LD
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Selecting Resources for a User
Skills
Multimedia Production Method
Self-manage (10) Evaluate (9) Synthesize
(8) Repair (7) Analyze (6) Apply
(5) Transpose (4) Interpret (3) Identify
(2) Memorize (1) Pay attention (0)



.
Performance
Aware Familiarized
Productive Expert
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Competency Equilibrium
Components of a LD reach competence equilibrium
when learning succeeds
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Conclusion

• Competency is
• a knowledge management / education goal
• an instructional engineering method tool
• a way to personalize learning during delivery
• a way to improve learning environments after
  delivery
• a central piece for ePortfolios and User models
• New generation tools are needed to support
  competency-based engineering, learning and
  tutoring
• New specification for competencies (Extend IMS-LD
  ?)
• Shared ontology for semantic annotatation
  learning designs

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ICALT-06, Kerkhade, The Netherlands,July 5-7,
2006 MERCI!Gilbert PaquetteLORNET and
CICE DirectorLICEF Research Center,
Télé-universitéwww.licef.teluq.uquebec.ca/gp

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