Adaptive Web-based Courseware Development Using Metadata Standards and Ontologies

Transcription

1 Adaptive Web-based Courseware Development Using Metadata Standards and Ontologies Lydia Silva Muñoz Former M.Sc. Student José Palazzo M. de Oliveira Professor at Federal University of RGS Porto Alegre - Brazil The The AdaptWeb AdaptWeb Project Project is is a a consortium consortium of of two two Universities Universities supported supported by by Brazilian Brazilian Research Research Council, Council, CNPq CNPq and and UFRGS. UFRGS.

2 Adaptive Courseware! An Adaptive Courseware can: Adapt the system behavior to the profile of the students! An Adaptive Web based Courseware can also: Interoperate using Web resources enabling the reuse of educative material created in the system context or in the context of other applications 2/21

3 Adaptation tools! A model of the Student Profile to conveys the knowledge about the student A model of the knowledge about the educative content based on metadata descriptions to convey: the meaning of each piece of content the correct way to use it (e.g. minimum speed connection) the possible ways to be assembled with others in order to obtain more complex learning object (e.g. courses) based in simple ones (e.g. topics explanations or exercises) 3/21

4 Interoperation tools! A Standard Metadata Model envisaged to describe e- learning content in a common language. (e.g. The Learning Object Metadata Model - LOM)! A binding to implement the Standard Model on the Web. (e.g. an RDF binding)! If needed, the definition of an Application Profile of the Standard Metadata Model that makes the standard suitable to the particular community without loss of compatibility 4/21

5 Implementing Adaptation Knowledge Space Hyperspace 5/21

6 The AdaptWeb Hyperspace Discipline Structure Discipline.dtd Topic Structure Topic.dtd Topic Support (Lectures,Exercices, Examples, etc.) 20 Topic.dtd Topic.dtd 6/21

7 Content Adaptation Engineering Physics Index Index Concept 1 Concept 2 Concept 3 Concept 1 Concept 2 Concept 2.1 Concept 2.2 Concept 1.1 Concept 2.1 Concept 2.2 Concept Concept /21

8 Content Adaptation Index Index Concept 1 Exercises $ Examples % Material C. Concept 2 Exercises $ Examples % Material C. Concept 3 Exercises % Material C. Concept 1 Exercises " Examples # Material C. Concept 2 Exercises " Examples # Material C. Concept 2.1 Exercises $ Examples % Material Concept 2.2 $ Examples % Material Concept 1.1 Exercises " Examples Concept 2.1 Exercises " Examples # Material C. Concept 2.2 " Examples # Material C. Concept Exercises $ Examples % Material Concept Exercises # Material C. 8/21

9 9/21

10 AdaptWeb Architecture Author Authoring STORAGE Web Resources Recommendation Ontology Enrichment Domain Knowledge Ontology Automatic Metadata Generation Hyper- Space xml html Recommended Web Resources ADAPTATION Student Monitoring Log and Login Student Knowledge Ontology Adaptive Content Selection Adaptive Presentation Student 10/21

11 Cognitive Learning Style Identification Student Auto Assesment General View Registered activityes of the Student Student Model Student Learning Trajectory Workflow Cognitive Learning Style Student Model Settlement Metadata Standard for Educative Content Knowledge Space Model Design Knowledge Space Model Define the Learning Trajectory Workflow Define the Application Profile of the Standard Standard Application Profile Automatic Metadata Generation Metadata Instances Hyperspace Model Design Hyperspace Instances Metadata Augmentation Hyperspace Model Educative Content Authoring 11/21

12 Structuring Metadata! A loosely coupled metadata repository supported by a Web Ontology! Supports a powerful adaptation mechanism Inference can be used to achieve adaptation (e.g. transitive properties indicating prerequisite conditions can be automatically computed)! Supports reusable learning objects Metadata based on standard vocabularies maintained on the Web as reference points for semantics enable the resolution of interoperation at the semantic level 12/21

13 13/21 CAiSE 2004 Content Knowledge Ontology awo:topic awo:isavailableto awo:isavailableto awo:customizes awo:has Prerequisite awo: ispartof awo: learningpath awo:course awo:discipline awo: LearningObject awo:support awo:exercise lomedu:difficulty lom-edu:difficulty awo:example awo:complementary dcterms:rfc1766 awo:contributor awo: NetworkConnection lom-tech: TechnologyRequirement xsd:string lom-edu: InteractivityType lom-tech: requirement awo:creator dc:description lom-tech:location lom-meta: metadatascheme dc:language lom-edu: interactivitytype lom:entity vcard:fn awo:e-support awo:is RecommendedBy awo:ispartof awo: supportsto lom-cls: Taxonomy awo:subject lom-edu:learning ResourceType lom-edu: learningresource Type awo:keyword awo:keyword rdfs:subclassof

14 Properties Refinement rdf:property rdf:type dc:relation rdf:subpropertyof dcterms:requires rdf:subpropertyof awo:hasprerequisite daml:inverseof awo:isprerequisiteof! Inverse and transitive properties are explicitly declared! Any Web agent (e.g. an RDF agent) can understand the super-property dcterms:requires and to interpret the ontology relation awo:hasprerequisite with the more general semantics of the known property 14/21

15 Properties Refinement rdf:subpropertyof rdf:type lom-life:educationalvalidator dc:contributor awo:creator awo:subject awo:keyword awo:learningpath awo:isrecommendedby daml:inverseof dc:creator dc:subject daml:inverseof awo:recommends dc:source awo:prevlearningpath rdf:property awo:supportsto dcterms:isversionof dc:relation daml:inverseof awo:issupportedby awo:customizes dcterms:ispartof daml:inverseof awo:iscustomizedby dcterms:requires awo:hasprerequisite awo:ispartof daml:inverseof awo:haspart awo:isavailableto daml:inverseof dcterms:hasversion daml:inverseof awo:hasavailable awo:isprerequisiteof dcterms:haspart 15/21

16 Automatic Metadata Generation! Each time new educative content is created into the system context a wrapper, aware of the semantics of the XML tags of the Hyperspace files and the creation context (e.g. the mother language of the teacher to infer the language of the learning object), automatically generates RDF statements describing learning objects in the Knowledge Space, such as:... 16/21

17 Automatic Metadata Generation!... 1) Disciplines and Courses plus the information of which courses customize which disciplines 2) Topics explanations, exercises, examples and other kind of complementary material plus the information of which material support which topic explanation and the other relations! The correct sequence of topics into a discipline and the ispartof relation among them are inferred from the XML sequencing and nested position of elements 17/21

18 Automatic Metadata Generation Content Knowledge Ontology Instance Domain Taxonomy Instance partof relation Direct Methods. xml Introduction xml Hyperspace Linear SystemsOf Equations. xml Gauss Method. xml Numerical Methods. xml Introduction Linear Sistems Gauss Method Numerical Methods subject Direct Methods subject subject GAUSS METHOD subject subject LINEAR DEPENDENCY LINEAR ALGEBRA LINEAR SYSTEMS LINEAR SYSTEMS DIRECT SOLUTION METHODS GAUSS METHOD WITH PIVOTING ALGEBRA LINEAR TRANSFORMATIONS LU FACTORIZATION Knowledge Space ABSTRACT ALGEBRA CANONICAL FORMS LINEAR SYSTEMS ITERATIVE SOLUTION METHODS CHOLESKY FACTORIZATION JACOBI METHOD 18/21

19 Student Ontology Content Knowledge Ontology Class Student Ontology Class st:cognitive Learning Style st:has LearningStyle st:has LearningGoal awo:course awo:topic st:has KnowledgeOn st:student st:location LearningTrajectoryWF st:wants Tutorial awo:has NetworkConnection awo:network Connection xsd:string xsd:boolean 19/21

20 Final remarks! This paper describes an in progress project for adaptive content automatic computation of Web-based courses, according to selected programs and student s profile! A new application profile of the Standard LOM based in a RDF binding is defined and implemented in order to give the system the capacity of share learning objects across the Web! A formal Web ontology supports metadata descriptions based in the constructed application profile. Such a ontology sets the stage to use inference in the computation of adaptability 20/21

21 Final remarks! A simple taxonomy models the learned domain. Links relating each learning object with elements in the taxonomy indicates to which topic in the domain the learning object is associated! Authoring software is provided for syllabus generation, supported by XML standard files (i.e. The Hyperspace)! A wrapper automatically generates the metadata instances (i.e. The Knowledge Space). The automation is based on the XML structure of the Hyperspace and the knowledge available about the context creation of the educative content! The learner s profile is modeled by a Web Ontology that support adaptation tasks 21/21