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A Knowledge-based Approach to Describe and Adapt Learning Objects

A Knowledge-based Approach to Describe and Adapt Learning Objects. Amel Bouzeghoub, Bruno Defude, J.-F. Duitama, Claire Lecocq GET/INT - France. Outline. Context and goals Three levels model Domain model User model Learning object model Learning strategies and adaptive process

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A Knowledge-based Approach to Describe and Adapt Learning Objects

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  1. A Knowledge-based Approach to Describe and Adapt Learning Objects Amel Bouzeghoub, Bruno Defude, J.-F. Duitama, Claire Lecocq GET/INT - France ProLearn-iClass Thematic Workshop – 3-4 March 2005, Leuven

  2. Outline • Context and goals • Three levels model • Domain model • User model • Learning object model • Learning strategies and adaptive process • Conclusion and on-going work

  3. Context and goals • Numerous pedagogical resources of different nature (html pages, multimedia, web-services, etc.) • accessed, exchanged between teachers with no dedicated tool • first common knowledge bases • Provide an environment of • authoring • increase the productivity of the teacher by supporting the reuse and composition of contents • presentation of pedagogical contents adapted to the learner A model of reusable learning objects

  4. Three levels model Domain Concept Elementary LO Complex LO Adapted courses Role Role Role Learning objects Zoom on a complex LO Users

  5. Relational Databases Object-Relational D.B G. I. S. Relational Algebra Relational Calculus Domain model Relationships Broader-Narrower Rhetorical Computer Sciences Databases Extend Contrast

  6. Domain knowledge (dynamic) Preferences User model U14 (definition, medium) language media bgcolor Computer Sciences (-, nv) Databases french video white Relational Databases Object-Relational D.B G. I. S. Extend S.Q.L Relational Calculus Relational Algebra (description, low) Contrast

  7. Learning object model Contents LOid Composition Prerequisites Acquisition Function ILO LO3 LO1 alt seq LO2 par seq LO4 Educational Characteristics

  8. Learning object model (2) C10 Descriptive meta-data (LOM) Prerequisites Content Acquisition {description, application} (description, low) Computer Sciences language media author Databases (definition, low) french Lecocq Object-Relational D.B G. I. S. Relational Databases Extend text video S.Q.L Relational Calculus Relational Algebra Contrast +(description, high)

  9. Component Base UM UM Learning strategies and adaptive process • Course-based learning and goals-based learning Delivering graphs preferences prerequisites choice Delivering Authoring

  10. Conclusion and on-going work • A semantic model • describes domain, user and learning objects • provides authors and learners powerful mechanisms to manage, reuse, compose learning objects, to organize concepts and to adapt content to users • A prototype is being implemented • models are implemented with RDF • SeRQL query language is used as inference layer (Sesame) • Open problem : distributed architectures

  11. Thank you

  12. Complementary slides ProLearn-iClass Thematic Workshop – 3-4 March 2005, Leuven

  13. User model U.M = <preferences, domain-knowledge> domain-knowledge = {<Learner, role, concept, educational-state>} 1) Learner = User Id 2) role : {analyze, apply, compare, define, demonstrate, describe, evaluate, experiment, history, illustrate, introduce, summarize} 3)concept Domain model 4) educational-state:(“Not-visited”, “Visited”, knowledge-level) knowledge-level = {“very low”, “low”, “medium”, “high” , “very high”}

  14. Learning object model • Component = piece of software (document, set of web pages, program, …) accessible via an URI • Unit of reuse and composition • Described by a set of meta-data • IEEE LOM standard • Extended with semantic description

  15. Semantic of simple operators

  16. Failure Handling • Previous expressions do not handle unsuccessful component access • A component is unsuccessful when its acquisition function returns FAIL • Failure expression : •  Ci SEQ Cj : after a failure of Ci try Cj •  Cin SEQCin : after a failure of Ci try again n times (at most) •  Ci SEQ FAIL : after a failure of Ci propagates the failure to the overall component

  17. Course-based Learning Process Composition Composition Filtering Filtering Filtering Filtering User User Component Component   S1={ S1={ C C } } C C k k l l S2 S2 S1 S1 S3 S2 S2 j j j j C C expansion expansion prerequisites prerequisites preferences preferences choice choice delivering delivering j j UM rewriting UM UM UM UM UM UM

  18. Goals-based Learning

  19. specialise Concept Rdf :subClassOf Rdfs :Class Contrast Extend Rdf :Property RDBMS RelationalCalculus RelationalAlgebra RhetoricalRelation DBMS Rdf :type Rdf :type Rdfs :range Rdfs :domain Rdf :subPropertyOf Rdf :subPropertyOf Rdf :subPropertyOf Rdfs :range Rdf :Type Rdfs :domain RDFS RDF Computer science specialise specialise Programming languages specialise specialise specialise extend OR DBMS OO DBMS specialise specialise contrast

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