1. CGIAR On-line Learning Resources (OLR) A learning community of practice

2. World Agroforestry Centre Nairobi Kenya IITA Ibadan Nigeria WARDA Cotonou Benin Bioversity International Rome Italy CIP Lima Peru CIAT Cali Colombia CIMMYT Mexico City Mexico IRRI Los Baños Philippines CIFOR Bogor Indonesia WorldFish Penang Malaysia IWMI Colombo Sri Lanka ICRISAT Patancheru India ICARDA Aleppo Syrian Arab Republic ILRI Nairobi Kenya IFPRI Washington, DC USA CGIAR system

3. Training challenges  CGIAR ‘training’ is in high demand by national partners and clients  Limited human and financial resources for capacity strengthening  Centres work mostly in isolation often leading to duplication of efforts  Learning materials are dispersed and in many different formats  Focus remains on more traditional learning approaches

4. Opportunities  Create a CGIAR training community of practice that works together to address the learning needs of our partners in research, development, training and education  Make use of recent advances in ICT to avail CGIAR learning resources in agriculture and natural resources management to an international learning community

5. First attempt: CG-LRC

6. CG-LRC  Searchable on-line database  Single location (maintenance)  Metadata standards unclear (federated searches?)  Standardization of resources  Re-usability and inter-operability  Software (proprietary -RoboHelp)  Link with other ICT-KM projects/activities (AGROCURI, VRC, VASAT, V-Lib, E-Pub)?

7. Learning Objects

8. RLO Characteristics 1. Digital/web-based 2. Reusable (different types of courses) 3. Self contained (focus on topic/learning objective) 4. Small in size (2-15’ learner attention) 5. Standardized (free of look and feel) 6. Flexible (easy to update, quality resources) 7. Interoperable (different platforms/other tools) 8. Suited for new types of learners (learner centered) 9. Cost effective (avoid duplication/redundancy) 10. Searchable (tagged with metadata)

9. Learning Object Metadata (LOM)

10. Repositories & Referatories

11. Partnering with ARIADNE

12. Why ARIADNE?  Recognized authority on LOM  Open source vs proprietary software CostCost Technical supportTechnical support  Backed by continuous research (European universities)  Global – allows federated searches  Integration with open source LCMS  Automating data entry  Visualization techniques

13. CGIAR – Training Knowledge Pool  Uses CGIAR LOM Application Profile  Is integrated with LCMSs  Open source software  Original resources/formats  Focus on learning objects  Re-usability & inter-operability  More linking/usage potential

14. Learning Object Metadata (LOM)

15. CG LOM Core  Which elements can be used to describe CGIAR learning resources  Which namespace they belong to  Which organizations have registered, defined and described the namespaces  Which conditions the individual data elements need to satisfy when applied in the OLR

16. CG LOM Core  To describe its learning resources in a manner that best suits its content, purpose and audience  To define a core set of metadata elements, which describe, document and register the CG learning object metadata core (CG LOM Core)

17. Dublin Core-based APs  Education Network Australia (EdNA)  Electronic learning resources for engineering of FAILTE  Gateway to Educational Materials (GEM)  Agricultural Metadata Element Set (AgMES)

18. Mapping of different APs

19. Review of other LOM APs  ARIADNE  Biosci Education Network (BEN) Collaborative  Canadian Core Learning Resource Metadata Application Profile (CanCore) initiative  Context eLearning with Broadband Technologies (CELEBRATE)  Curriculum Online  Health Education Assets Library (HEAL)  IMS  Norsk LOM (NORLOM)  UK LOM Core

20. Comparison

21. CGIAR On-line Learning Resources (1) http://learning.cgiar.org

22. Some issues  Technology driven solutions?  In-service training vs education  Scientist contributions/incentives  Agriculture = practice & context  Metadata – library stuff?  Re-use of LOs