Download Report


Desislava Paneva
Institute of Mathematics and Informatics
Bulgarian Academy of Sciences
[email protected]
Presentation overview
• Basic concepts and characteristics of digital libraries
• Digital libraries and eLearning systems vis-à-vis
• Student modelling – main issues, standards, Semantic
web approach for model constructing, examples
• Main elements of the student model
• Student ontology
• Scenario for implementation of student ontology
• Conclusion and future work
• References
Basic concepts and characteristics of digital libraries
Digital libraries (DL) are organised collections of digital content made
available to the public, offering services and infrastructure to support
preservation and presentation of visual and knowledge objects anytime and
The main characteristics of digital libraries:
Ability to share information
New forms and formats for information presentation
Easy information update
Accessibility from anywhere, at any time
Services available for searching, selecting, grouping and presenting digital
information, extracted from a number of locations
Personalization and adaptation
Contemporary methods and tools for digital information protection and
Different types of computer equipment and software
No limitations related to the size of content to be presented, etc.
Basic concepts and characteristics of digital libraries
The functionalities and advanced services of contemporary digital libraries:
Multi-layer and personalized search, context-based search, relevant feedback
Resource and collection management
Metadata management
Semantic annotation of digital resources and collection
Multi-object and multi-feature search
Different media type search, etc.
Hypermedia digital library
Grid-based infrastructure
Hyperdatabase infrastructure
Digital libraries and eLearning systems vis-à-vis
• Knowledge-on-Demand
• Just-in-time methods for supporting knowledge
• Provision of more efficient and more flexible tools for transforming digital
content to suit the needs of end-users
• Sustaining of resources
• Resource description
• Heterogeneous resources in a coherent way
• Intellectual property rights
• Flexible architectures that provide interoperability
• Innovative services
• Effective user modelling tools, etc.
Student modelling
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
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)
Main tools for constructing a student model ontology are:
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.
Student modelling - examples
SeLeNe learner profile
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
Student modelling - examples
An excerpt of ELENA conceptual model for the learner profile with main concepts
Project ELENA – Creating a Smart Space for Learning (01/09/2002 – 29/02/2005)
Main elements of the student model
General student information
StudentPersonalData - StudentName, StudentSurname, StudentId,
StudentAge, StudentPostalAddress, StudentEmail, StudentTelephone
StudentPreference – StudentObjectGroupingPreference,
StudentObjectObservationStyle, StudentMultipleIntelligence,
StudentPhysicalLimitation, StudentLanguagePreference
StudentBackground - StudentLastEducation, StudentExperience
StudentMotivationState - StudentInterest, StudentKnowledgeLevel
Information about the student’s behaviour
Student ontology
Student ontology
Student ontology
Object properties:
Inverse properties: hasA and isAOf, where A is the name of some class or subclass
Examples: hasStudentBackground and isStudentBackgroundOf
hasStudentExperience and isStudentExperienceOf
hasStudentKnowledgeLevel and isStudentKnowledgeLevelOf, etc.
Other properties: PreferringObjectGrouping, FollowingObjectObservationPath,
Wishing, etc.
Restriction: Existential quantifier () and the Universal quantifier ()
Examples: “
Student ontology
The figure depicts the
class of the described
student ontology. The
student background is
based on the
and StudentExperience.
The last education is
certified by any diploma
or certificate with a
written qualification
type. This document is
issued by a certain
organization and usually
has a validation period.
The student experience
implies type, description
and duration.
Scenario for implementation of student ontology
Personalized search
 Student motivation state - student knowledge level (beginner, advanced, high)
and student interest
 Student learning goal
 Student background
 Student behaviour in the digital library – chosen objects/collections, object
observation path, etc.
 Student object observation style
 Language preference
 Student physical limitations, etc.
Context-based search
 Student preferences – object grouping preference, etc.
Conclusion and future work
Modelling and creation of domain ontology, describing the knowledge for the
digital objects of the digital library.
Merging this ontology with the presented student ontology
Development of semantic-based DL services such as semantic annotation of
digital 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.
LTSC Learner Model Working Group of the IEEE (2000) IEEE p1484.2/d7, 2000-11-28 Draft Standard for
Learning Technology - Public and Private Information for Learners, Technical report Available Online:
Smythe, C., F. Tansey, R. Robson (2001) IMS Learner Information Package Information Model Specification,
Technical report Available Online:
Chen, W., R. Mizoguchi (1999) Communication Content Ontology for Learner Model Agent in Multi-Agent
Architecture, In Proceedings of AIED99 Workshop on Ontologies for Intelligent Educational Systems
Kay, J. (1999) Ontologies for reusable and scrutable student model, In Proceedings of AIED99 Workshop on
Ontologies for Intelligent Educational Systems
Heckmann, D., A. Krueger (2003) A User Modeling Markup Language (UserML) for Ubiquitous Computing, In
Proceedings of the Ninth International Conference on User Modeling, Berlin Heidelberg: Springer, pp. 393-397
OWL Web Ontology Language Overview. W3C Recommendation 10 February 2004, Available Online:
Self, J. (1990) Bypassing the intractable problem of student modelling. In C. Frasson & G. Gauthier (Eds.),
Intelligent Tutoring Systems: At the Crossroads of Artificial Intelligence and Education. New Jersey:
Lane, C. (1998) Gardner’s multiple IntelligenceAvailable online:
Lane, C. (2000) Learning styles and multiple intelligences in distributed learning/IMS projects. San Clemente,
CA, The Education Coalition (TEC)
Sison, R., M. Shimura (1998) Student Modelling and Machine Learning. International Journal of Artificial
Intelligence in Education volum 9, pp. 128-158