1. Ontology-based Student Modelling Desislava Paneva Institute of Mathematics and Informatics Bulgarian Academy of Sciences dessi@cc.bas.bg

2. Presentation overview Student modelling - main issues Student modelling standards Student modelling - Semantic web approach for model constructing Student modelling examples Main elements of the student model Student ontology Scenario for implementation of student ontology Conclusion and future work

3. Student modelling – main issues Student modelling can be defined as the process of acquiring knowledge about the student in order to provide services, adaptive content and personalized instructional flow/s according to specific student’s requirements. Main questions: Student interests: What is the student interested in? What needs to be done or accomplished? Student preferences: How is something done or accomplished? Student objectives and intents: What the student actually wants to achieve? Student motivation: What is the force that drives the student to be engaged in learning activities? Student experience: What is the student’s previous experience that may have an impact on learning achievement? Student activities: What the student does in the learning environment? ….

4. Student modelling standards Incorporation between IEEE LTSC’s Personal and Private Information (PAPI) Standard and the IMS Learner Information Package (LIP)

5. Student modelling – Semantic web approach Earliest ideas of using ontologies for learner modelling (Chen&Mizoguchi, 1999). Use of ontologies for reusable and “scrutable” student models (Kay, 1999) Ontology modelling languages - OIL, DAML+OIL, RDF/RDFS, OWL, etc. Ontology development tools - Apollo, LinkFactory®, OILEd, OntoEditFree, Ontolingua server, OntoSaurus, OpenKnoME, Protégé-2000, SymOntoX, WebODE, WebOnto, OntoBuilder, etc. Ontology merge and integration tools – Chimaera, FCA-Merge (a method for bottom-up merging of ontologies), PROMPT, ODEMerge, etc. Ontology-based annotation tools – AeroDAML, COHSE, MnM, OngtoAnnotate, OntoMat-Annotizer, SHOE Knowledge Annotator, etc. Ontology storing and querying tools - ICS-FORTH RDFSuite, Sesame, Inkling, rdfDB, RDFStore, Extensible Open RDF (EOR), Jena, TRIPLE, KAON Tool Suite, Cerebra®, Ontopia Knowledge Suite, Empolis K42, etc. Main tools for constructing a student model ontology are:

6. Student modelling - examples The Self e-Learning Networks Project (SeLeNe) is a one-year Accompanying Measure funded by EU FP5, running from 1st November 2002 to 31st October 2003, extended until 31st January 2004 SeLeNe learner profile

7. Student modelling - examples Project ELENA – Creating a Smart Space for Learning (01/09/2002 – 29/02/2005) An excerpt of ELENA conceptual model for the learner profile with main concepts

8. Main elements of the student model General student information StudentPersonalData - StudentName, StudentSurname, StudentAge, StudentPostalAddress, StudentEmail, StudentTelefone StudentPreference - StudentMultipleIntelligence, StudentLearningStyle, StudentPhysicalLimitation, StudentLanguagePreference StudentBackground - StudentLastEducation, StudentExperience StudentMotivationState - StudentInterest, StudentKnowledgeLevel StudentLearningGoal Information about the student’s behaviour ConceptCompetenceLevel ModuleCompetenceLevel CourseCompetenceLevel ModuleStudyTime TestSolvingStatus

9. Student ontology

10. Object properties: Inverse properties: hasA and isAOf, where A is the name of some class or sub-class Examples: hasStudentBackground and isStudentBackgroundOf hasStudentExperience and isStudentExperienceOf hasStudentKnowledgeLevel and hasStudentKnowledgeLevelOf, etc. Restriction: Existential quantifier (  ) and the Universal quantifier (  ) Examples: “  hasStudentMotivationState StudentMotivationState” quantifier property filler

11. Scenario for implementation of student ontology Personalized search on the base of:  Student knowledge level (beginner, advanced, high) and student interest  Student learning goal  Student background  Student behaviour in the learning environment – concept competence level, module competence level, course competence level, test solving status  Language preference  Student physical limitations, etc.

12. Conclusion and future work Modelling and creation of ontology that describes the learning domain. Merging this ontology with the presented student ontology. Development of semantic-based services such as semantic annotation of learning objects, indexing, metadata management, etc. Development and implementation of semantic search, personalized search, context-based search, multi-object search, multi-feature search, etc. using the merged ontology and following the implementation scenario.