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One of mEducator Research Goal:

One of mEducator Research Goal:. Facilitate search and retrieval in the resource sharing process

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One of mEducator Research Goal:

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  1. One of mEducator Research Goal: • Facilitate search and retrieval in the resource sharing process • How: analyze the type of information that can be generated during the repurposing activity and find ways to coordinate it with all the information spaces that pertain to a LR to improve the search and retrieval capability of the content sharing system.

  2. premises • How do we search? • Focused VS exploratory search • How do we repurpose?

  3. LR Repurposing model Repurposing links: Typed (repurposing contexts) Weighted repurposed to Repurposing Description LR1 repurposed to Why (intended goals) Activity (intended usage) Engagement? Diagnostic reasoning? Perceptual discrimination? Conceptual model? LRm repurposed to LRn

  4. Adding the social dimension… LRm Links space Tags space Authors/Users space Reuse & Activity space Description LR metadata scheme

  5. Adding the social dimension… LRm Links space Tags space Authors/Users space Reuse & Activity space Multimedia Processing Agents Description LR metadata scheme

  6. Adding the social dimension… LRm Links space Tags space Authors/Users space Reuse & Activity space Multimedia Processing Agents Description LR metadata scheme

  7. Key aspects of the proposal • Authors/users of the LR describe the repurposing activity that is anchored to the type of learning outcome and the intended strategy of use • Usage of a clustering and association building method that supports exploratory search (so far overlooked)

  8. Exploratory Search Search Process Exploratory Search Focused Search I have a broad intuition of what I’m looking for I know exactly what I’m looking for

  9. Exploratory Search Search Process Exploratory Search Focused Search The search process is integral to query refinement

  10. Possible solutions Search Process Exploratory Search Focused Search ? vs Metadata User-generated tags

  11. The problem we’re interested in Search Process Exploratory Search Focused Search Metadata User-generated tags Repurposing information

  12. The problem we’re interested in: “Analogical” retrieval “Query Matching” Retrieval Exploratory Search User Query “Analogical” Retrieval Metadata (LOM) User-generated tags Repurposing: Links Repurposing: Usage descriptions

  13. “Query matching” vs “Analogical” retrieval “Query Matching” Retrieval Similar experiences not necessarily described by same terms Innovative practices described with unknown terms Shortcomings: • High precision • Low recall • Recall can be enhanced by semantic expansion Lexical-semantic similarity

  14. “Query matching” vs “Analogical” retrieval “Analogical” Retrieval Similarity notion between Learning Resources is based on - structural properties - abstracting functionalities and roles from descriptions Foundations: • High recall • Low precision • Precision can be enhanced by post-hoc “query matching” filters Description patterns similarity

  15. The method • Moving from vectorial description of the LR to unsupervised classifications of descriptions • Create a space of associations • Explore this space by coordinated usage of analogical and standard query matching

  16. Step 1: LR vectorial representation Description Description LR metadata scheme Tagsspace Reuse & Activity space Linksspace LR vectorial representation

  17. Step 2: classification and positive features extraction 1, 3, 8, 9 1, 3, 4, 8 CT1 CL1 CL3 CL4 Unsupervised classifier (Kohonen SOM) Positive features are the most shared ones in the class L1, L2 U3, U5 T1, T6 L 3 3, 8, 9, 10 CR1 CR2 CU1 5, 7, 8 CL2 CU3 CT3 R4 U1 CT2 5, 7, 8 CR3 2, 6, 7, 10 CU2

  18. Step 3: deriving the associations L1, L2 U3, U5 T1, T6 L 3 1, 3, 8, 9 1, 3, 4, 8 3, 8, 9, 10 CR1 CT1 CR2 CL1 CU1 5, 7, 8 CL2 CU3 CT3 U1 CT2 5, 7, 8 CL3 CL4 CR3 2, 6, 7, 10 R4 CU2 T1,T6 U3, U5 U1 R4 L1,L2 T1,T6 L1,L2 U3, U5

  19. Step 4: joint use QR and AR 1, 3, 8, 9 1, 3, 4, 8 CT1 CL1 CL3 CL4 L1, L2 U3, U5 T1, T6 L 3 3, 8, 9, 10 CR1 CR2 CU1 5, 7, 8 CL2 CU3 CT3 U1 R4 CT2 5, 7, 8 CR3 2, 6, 7, 10 CU2 T1,T6 U3, U5 U1 R4 L1,L2 T1,T6 Associations are signalled of their classes share a number of elements above a user set threshold L1,L2 U3, U5

  20. Step 4: joint use QR and AR L1 T6 U1 R4 L1,L2 U3,U5 User Query Query matching 1th Module Analogical Retrieval 1th module CL1 CT1 CU2 CR3 d1 d7 CL1 CU1 Analogical Retrieval 2nd module Analogical Retrieval 3rd module L1,L2 T1,T6 d1 d3 d4 d8 d9 d10 Lexical Filter Analogical Retrieval 4th module d1 d3 d8 Query matching 2th Module d3 d8 Lexical Filter

  21. The opportunities…. • No commitmente to a priori organization of the metadata • Independent classification on subsets • Incremental addition of other information spaces • Capturing contexts and practices that do not share a common vocabulary yet

  22. The challenges…. • Periodic indexing and re-classification • Some info that is input to classification spaces more difficult to automatically collect in a distributed repository model • Usable interfaces to support the exploratory process • Evaluation of suitable metrics for the classification

  23. Thanks for your attention! Questions?

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