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Knowledge sharing & recommendation services

Knowledge sharing & recommendation services. Goal of WP3. ‘The main task is to investigate, design and implement social recommendation and knowledge sharing mechanisms suitable for ... project-centred scenarios’

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Knowledge sharing & recommendation services

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  1. Knowledge sharing & recommendation services

  2. Goal of WP3 • ‘The main task is to investigate, design and implement • social recommendation and • knowledge sharing mechanisms • suitable for ... project-centred scenarios’ • These [collaborative recommendation] approaches are complemented by latent semantic analysis, ... based on the content of repositories

  3. Knowledge sharing of peers through ad hoc transient communities • Example of lsa based recommendation • Current project in OUNL • Focus on peer support • is example of recommendation and knowledge sharing • helps solve teacher bandwidth problem • increases competencies of both tutor and tutee

  4. Essence of the approach • analyse ‘tutee’ question using latent semantic analysis • find and select ‘suitable’ peer-tutors • suitable means competent, available, eligible • set up wiki and create ad-hoc, transient community of tutee and a number of tutors around it • seed wiki with proto-answers, discovered by lsa • log results in for instance FAQ, user portfolio, community database, for future use

  5. Moodle LSAmodule Tutor locator

  6. Moodle LSAmodule Tutor locator

  7. Moodle LSAmodule Tutor locator

  8. Moodle LSAmodule Tutor locator

  9. The positioning problem • Imagine • some student, not necessarily involved in a formal programme, with a learning target • (s)he has several prior (certified or not) competencies, • (s)he enters a ‘learning network’ • How to • map the target on the learning network? • map his or her prior competencies?

  10. Learner positioning

  11. LSA and positioning: our current goals • Develop specs for the use of Latent Semantic Analysis for positioning • Specify, develop and test a prototypical ‘positioner’ • With respect to their validity and reliability, compare human and LSA-based practices of honoring prior competencies

  12. What is LSA? assetB element in user portfolio assetA assetC

  13. 1 2 3 j m ape 5 11 0 left 1 2 33 the 110 156 144 kind 25 19 0 i fr(ij) n f(mn) How does it work? documents terms

  14. How does it work? • start with term X document matrix • many zeros, low average cell freq -> sparse matrix • use stop rules • use stemming? • apply other ‘rules of thumb’ • use vector representation per document through SVD (cf. factor analysis)

  15. explicit semantics (metadata approach), requires effort upfront, during development of tasks and portfolios; requires ontology with inference rules; improves over time (semantic web) latent semantics, works during runtime, should be fast enough, no explicit semantics, hence no improvement over time (GTP) Compare and contrastan additional goal

  16. Goal of WP3 • ‘The main task is to investigate, design and implement • social recommendation and • knowledge sharing mechanisms • suitable for ... project-centred scenarios’ • These [collaborative recommendation] approaches are complemented by latent semantic analysis, ... based on the content of repositories

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