Pythagoras Karampiperis, Demetrios Sampson e-mail:{pythk, sampson}@iti.gr

1. Enhancing Educational Metadata Management Systems to support Interoperable Learning Object Repositories Pythagoras Karampiperis, Demetrios Sampson e-mail:{pythk, sampson}@iti.gr

2. What the problem is ? • A large number of digital learning resources exist, being already described using different metadata models or even different application profiles of the same metadata model • “harmonising” this, by expecting that these resources will be eventually re-described in a standard and universally accepted model or profile might be too much to anticipate … • in order to avoid disregarding the possibility of searching and re-using LOs in these repositories, certain functional requirements should be considered when designing LRM Management Systems.

3. Design Requirements

4. Architecture of a LRM Management System

5. Architectural Layers • Interface Layer:A layer visible by the users of the LRMMS. It contains all the components of the user-interface. These are the XML editor/wizard, the management interface, the publishing interface and the map generator. • Non-visible Layer:contains all the repositories involved and the operations, which are performed. The repositories involved are the Learning Resource Metadata repository, the XML Schema repository and the XML Translation Maps repository and the operations are validation and mapping

6. Information Storage • XML Schemas Repository: contains the XML Schema files for the educational metadata models • Learning Resource Metadata Repository: stores the metadata description of the learning objects • XML Transformation Maps Repository: Transformation maps should be automatically generated by a corresponding mechanism, by associating a number of elements of one metadata schema to a number of elements of another schema

7. Validation Process Flow Chart

8. Mapping Process

9. Multi-tiered Database Model (1) Ensures • processing and storage effectiveness • increased opportunity for security • portability and extensibility

10. Multi-tiered Database Model (2) LRM Management system is partitioned into: • Client application: provides a user interface on the user's machine. Ideally, it knows nothing about how the data is stored or maintained. • Application server (middle tier): resides in a central networking location accessible to all clients and provides common data services. Coordinates and processes requests and updates from multiple clients and handles all the details of defining datasets and interacting with the database server. • Remote database server: provides the relational database management system (RDBMS).

11. Advantages (1) • Encapsulation of business logic in a shared middle tier. Different client applications all access the same middle tier. • Thin client applications. Client applications can be written to make a small footprint by delegating more of the processing to middle tiers. Not only are client applications smaller, but they are easier to deploy. Thin client applications can be distributed over the Internet for additional flexibility.

12. Advantages (2) • Distributed data processing. Distributing the work of an application over several machines can improve performance because of load balancing, and allow redundant systems to take over when a server goes down. • Increased opportunity for security. This model provides the capability of isolating sensitive functionality into tiers that have different access restrictions. This provides flexible and configurable levels of security. Middle tiers can limit the entry points to sensitive material, allowing controlling access more easily.

13. EMMA – A prototype implementation (1) • The architectural design of the EMMA toolkit is based on • multi-tier database model • use of Memory Based XML Native Repositories conforming with the IMS Digital Repositories Interoperability – Core Functions Information Model specification • Use of the Simple Object Access Protocol (SOAP). • The metadata produced by the EMMA are conformed with the IEEE Learning Object Metadata 1484.12.1-2002 Standard.

14. EMMA – A prototype implementation (2) EMMA supports: • authoring of educational metadata for the description of educational resources and full management of the produced metadata descriptions • integration of different metadata specifications (Dublin Core, IMS Metadata) through the use of an advanced fully automatic mapping mechanism •  metadata structural and semantic validation • metadata publishing over the Internet through Web Services and the use of SOAP protocol

15. EMMA – Authoring Wizard

16. EMMA – XML Viewer/Editor

17. EMMA Mapping approach … • Data-Driven mapping mechanism, that is, it creates the corresponding map between the attributes of various different schemas by examining the similarity of the data values that the attributes hold … • it does it in 3 nested parts by measuring the percentage of similarity between the data values of the attributes against some predefined similarity threshold value that “defines” the “minumum accepted similarity” between the data values of 2 attributes as a criterion to accept the mapping as valid

18. Test setting … • map different representations of the same real-world entities using • Dublin Core • Ariadne • Gem • IEEE LOM • For the designing of the testing datasets, we used • the ISO 639 and ISO3166-1 standards as the language format scheme • the ISO 8601 standard as the date format scheme

19. Classification of Datasets to examine the effect of data values heterogeneity, 3 different datasets were identified with varying similarity, that is, • low (less than 40%) • Medium • and high (more than 70%) similarity the similarity between the data values of the attributes is measured by the following formula:

20. Evaluation Criteria Total evaluation criterion : the mean value of confidence, success and mistakes

21. Experiment 1: Robustness Test • Mapping is assumed by the algorithm to be valid • if the similarity between the entities is above a specific threshold • Efficiency of the proposed mapping algorithm • depends on the selection of the similarity threshold parameter • Robustness test • for three categories of the datasets according to the similarity between entities

22. Robustness Test

23. Experiment 2 : verification of effectiveness • to verify the effectiveness, we used a full datasets of IEEE LOM and Dublin Core metadata schemes representing the same learning objects (around 250 LOs) • to find that the result of the mapping algorithm were identical to the Annex B (Mapping to Unqualified Dublin Core) of IEEE 1484.12.1-2002 (Draft Standard for Learning Object Metadata) standard

24. IEEE LOM & DC Mapping (1)

25. IEEE LOM & DC Mapping (2)

26. IEEE LOM & DC Mapping (3)

27. Conclusions • New functional & design considerations should be satisfied to provide an effective LRM management system • Integrating data from different data sources is • time-consuming and labor-intensive • only few tools available to ease the task • The proposed architecture can be useful since it • Supports the automatic data integration from different metadata sources through the mapping mechanism • Ensures the structural and semantic metadata validity through the validation mechanism Open Issues • Real-time metadata integration over the Internet through Web Services

28. Contact Details Pythagoras Karampiperis and Prof. Demetrios Sampson pythk,sampson@iti.gr Informatics and Telematics Institute, Centre for Research and Technology Hellas, 42, Arkadias Street,Athens,GR-15234,Greece and Department of Technology Education and Digital Systems, University of Piraeus 150, Androutsou Street, Piraeus, GR-18534 Greece