Ontology-based Student Modelling

Desislava Paneva

Institute of Mathematics and InformaticsBulgarian Academy of Sciences

dessi@cc.bas.bg

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.