This document proposes a model and architecture for opening learner profiles across heterogeneous applications. It summarizes existing approaches that have low abstraction levels and lack mechanisms for exchanging profiles. The proposed approach uses a UML-based model with a core profile composed of sub-profiles. It is based on the WBEM standard and includes a service-oriented architecture with a tracking manager and profile service to facilitate collecting and accessing profiles distributed across different systems.

1. Opening Learner Profiles
across
Heterogeneous Applications
Triomphe Ramandalahy, Philippe Vidal, Julien Broisin
Université Paul Sabatier
Toulouse, France

2. Context
๏ Personalization of Web-based Learning Environments
๏ There is a need for collecting data about learning tools
and resources, users and activities
๏ We focus on ACTORS, and specially LEARNERS
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3. Issues to solve
๏ Learner profile: set of information related to a user or a
set of users
๏ Various information describing learners from various
points of view
๏ Information is distributed across heterogeneous
systems and applications
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4. Issues to solve
๏ Learner profile: set of information related to a user or a
set of users
๏ Various information describing learners from various
points of view
๏ Information is distributed across heterogeneous
systems and applications
๏ How to gather the whole information characterizing a
learner? How to share and reuse it?
➡Uniformely represent information to collect
➡Federate the various sources of information
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5. Outline
๏ Existing approaches
๏ Our proposal
๏ A model dedicated to learner profile
๏ A service oriented architecture
๏ Conclusions and future works
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6. Standardized approaches
๏ IEEE Personal And Private Information (PAPI - 2002)
‣ Personal information, competences, relations,
portfolio, security, ...
๏ IMS Learner Information Package (LIP - 2005)
‣ Additional information such as history, preferences,
affiliations or activities
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7. Specific approaches
๏ Reuse of External Profiles (REPro) [Eyssautier 08]
‣ Date of birth, living place, school year, first year (or
not) in this curriculum
๏ cosyQTY [Lazarinis 07]
‣ Personal information, objectives, knowledge, usage
of the sytem
๏ ...and many more
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8. Some lacks
๏ Low abstraction level
๏ No query language (or specific)
๏ No mechanism to exchange learner profiles between
heterogeneous applications
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9. Some lacks
๏ Low abstraction level
๏ No query language (or specific)
๏ No mechanism to exchange learner profiles between
heterogeneous applications
๏ Nearly impossible to define additional information
required for a specific learning application
๏ Very hard to share and reuse learner profiles
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10. Outline
๏ Existing approaches
๏ Our proposal
๏ A model dedicated to learner profile
๏ A service oriented architecture
๏ Conclusions and future works
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11. A model-driven approach...
๏ A UML-based modeling of learner profiles
‣ High abstraction level (extensibility)
‣ A core profile composed of several sub-profiles
๏ A system dedicated to the storage of learner profiles
๏ A service to modify/extend the learner profile
๏ A service to facilitate access to the dedicated storage
system
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12. ...based on an existing
standard
๏ Reuse of the “de facto” Web-Based Enterprise
Management (WBEM) standard elaborated by the
Distributed Management Task Force (DMTF)
๏ Natively dedicated to system, network and application
management
๏ The main advantages
‣ A Common Information Model (CIM)
‣ Some query languages (CQL and WQL)
‣ Some protocols to ensure communication between
heterogeneous applications and systems
‣ Several open source implementations
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13. Outline
๏ Existing approaches
๏ Our proposal
๏ A model dedicated to learner profile
๏ A service oriented architecture
๏ Conclusions and future works
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14. The global learner profile
๏ Based on the existing CIM User model
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15. A Technology Enhanced
Learning (TEL) core profil
๏ To represent any
TEL actor
(learner, teacher,
tutor, ...)
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16. A core profil for learners
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17. The cognitive sub-profile
๏ Integrates IMS LIP
categories
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18. The preference sub-profil
๏ Integrates interests,
preferences and
relationships
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19. The identification sub-
profile
๏ CIM User
๏ Additional information
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20. The metacognitive sub-
profile
๏ Various information
specified by
psychologists
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21. Outline
๏ Existing approaches
๏ Our proposal
๏ A model dedicated to learner profile
๏ A service oriented architecture
๏ Conclusions and future works
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22. The 3/3 architecture
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23. The 3/3 architecture
LEARNING ENVIRONMENT
LEARNING
SYSTEM 1
LEARNING
SYSTEM 2
LEARNING
SYSTEM N
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24. The 3/3 architecture
LEARNING ENVIRONMENT TRACKING ENVIRONMENT
LEARNING WBEM
TRACKING MANAGER
SYSTEM 1 framework
LEARNING
SYSTEM 2
LEARNING
TRACKING
SYSTEM N
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25. The 3/3 architecture
LEARNING ENVIRONMENT INTERMEDIATE TRACKING ENVIRONMENT
LAYER
AGENT
LEARNING WBEM
TRACKING MANAGER
SYSTEM 1 LEARNER framework
PROFILE
SERVICE
AGENT
LEARNING
SYSTEM 2
MODEL
PROFILE
AGENT
LEARNING SERVICE TRACKING
SYSTEM N
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26. Collecting the whole profile
from various applications
INTERMEDIATE
LEARNING ENVIRONMENT TRACKING ENVIRONMENT
LAYER
LEARNING AGENT WBEM
TRACKING MANAGER
SYSTEM 1 LEARNER framework
PROFILE
SERVICE
AGENT
LEARNING
SYSTEM 2
MODEL
PROFILE
AGENT
VIS. SERVICE
TOOL TRACKING
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27. The profil visualization tool
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28. Outline
๏ Existing approaches
๏ Our proposal
๏ A model dedicated to learner profile
๏ A service oriented architecture
๏ Conclusions and future works
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29. Conclusions
๏ Standardized approach: WBEM is natively integrated
within Microsoft and Linux operating systems
๏ The learner model
‣ High abstraction level (extensible)
‣ Integrates existing profils (IMS LIP, IEEE PAPI)
‣ Integrates metacognitive properties
๏ The management services
‣ Facilitate access to the tracking repository
‣ Make it easy to take into account additional
information
‣ Promote sharing and reusing of learner profiles
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30. Future works
๏ Experimentation with students of the Institute of
Technology in computer science (should have been
done this year but...)
๏ Automated extraction of users data enclosed within
the WBEM component of Microsoft Windows XP and
Vista
๏ Providing an intelligent helping system
‣ Detection of difficulty
‣ Analyse (data mining)
‣ Triggering contextual help
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31. Questions?
http://www.irit.fr/~Julien.Broisin
broisin@irit.fr
Thanks!
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