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Ontology-based Student Modelling
Desislava Paneva
Institute of Mathematics and InformaticsBulgarian Academy of Sciences
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
Student modelling – main issuesStudent 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?
• ….
Student modelling standards
Incorporation between IEEE LTSC’s Personal and Private Information (PAPI)
Standard and the IMS Learner Information Package (LIP)
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:
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
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
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
Student ontology
Student ontology
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
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.
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.