Learning Registry: Building a Foundation for Learning Resource Analytics


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My presentation at the LAK'12 conference. May 1, 2012

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Learning Registry: Building a Foundation for Learning Resource Analytics

  1. 1. + i sk The Learning t ow e ch nk Br Registry: Building a e n Bi h Jo ie Foundation for ar M Learning Resource al Analytics n tio na o Kl er Ji m nt II SRFunding from US Dept of Education US Dept of Defense Numerous contributions from the Learning Registry Open Source Community. Special thanks to NSDL and Navigation North
  2. 2. Why worry about learning resources?2010-09-27 LAK 12 2
  3. 3. Stovepiped repositories hold data and share through pipes like OAI-PMH Metadata grows stale as people refuse to update or even add at the outset Data exhaust is wasted: it’s not powering anything Learning resources languish “on the shelf” Data is locked in and locked down2010-09-27 LAK 12 3
  4. 4. Stovepiped repositories hold data and share via pipes... Vs. Data is shared in the open for aggregation, amplification, analysis…2010-09-27 LAK 12 4
  5. 5. Metadata grows stale as people refuse to update or even add at the outset (“We can’t plan on good metadata.”) Vs. Enrich the data with social usage data (paradata) to add nuance to metadata2010-09-27 LAK 12 5
  6. 6. Data exhaust is wasted−it’s not powering anything Vs. Data exhaust is collected to power the social life of learning resources2010-09-27 LAK 12 6
  7. 7. Learning resources languish on the shelf Vs. Resources live in a rich ecosystem2010-09-27 LAK 12 7
  8. 8. Data is locked in and locked down Vs. Democratization/Liberation of data2010-09-27 LAK 12 8
  9. 9. Data is shared in the open for aggregation, amplification, analysis… Learning Registry is a distributed system that permits sharing of metadata about learning resources …developed through an open community process that engages learning resource creators, publishers, curators, and consumers.2010-09-27 LAK 12 9
  10. 10. Learning Registry is a store- and-forward data exchange network—not a destination website, search engine, or repository—upon which diverse user services can be built. Enrich learning resource data with paradata to add nuance to metadata…2010-09-27 LAK 12 10
  11. 11. 2010-09-27 LAK 12 11
  12. 12. Annotational Paradata Kinematic Paradata Pedagogical Paradata 12 Refines descriptive Illustrates diffusion through Refines educational context metadata user actions and utilityTagged (as…) Clicked User demographics aggregatedfrequency by tag accessed, # of hits, … by paradata contributorRecommended / Included in Viewed Embedded (in…) watched, interacted with, …Group Collection# of times Correlated / Aligned (to…) Downloaded to standard, to grade level, to curriculumCommented / Discussed saved a local copy# of times – content of comments/discussionshandled as annotations Favorited / Included in Modified reused, created derivative work, added to, Personal Collection contextualized, personalized, enhanced,Rated foldered, listed, bookmarked, favorited, combinedavg rating by community, star rating, usability playlistedrating, … ===================== -implemented (in context…) Shared to Social MediaVoted frequency by platform (Facebook, Twitter…) -republished (as…)up/down, liked/disliked, … ===================== -researched================== -subscribed -saved/shared searches-related to other resources -linked to-cited -featured-awarded-ranked Data exhaust is collected to powerLAK 12 the social life of learning resources
  13. 13. Resources live in a rich ecosystem 2010-09-27 LAK 12 13
  14. 14. + Liberate the data…2010-09-27 LAK 12 14
  15. 15. + Liberate the data…2010-09-27 LAK 12 15
  16. 16. Many learning resource systems can be connected into the Learning Registry network through publish and consume APIs Learning Object Repositories, Teacher Portals, Search tools, Learning Management Systems, and Instructional Improvement Systems.2010-09-27 LAK 12 16
  17. 17. Currently the Learning Registry mainly contains metadata, published + from a number of collaborators, about resource classification and usage data. SHOW JIMS MAPPING HERE2010-09-27 LAK 12 17
  18. 18. Built on CouchDB document distribution system: easy to set up new nodes and replicate Metadata agnostic: uses JSON key-value pairs to encapsulate metadata in any schema Social usage data modeled on NSDL Com_para and activity streams Uses URL to identify resources; submitter identity verified by digital signature2010-09-27 LAK 12 18
  19. 19. Relationship Mining: surface relationships between people based on their attention to resources. What institutions, portals, or groups of users have shared/curated the same resource? User experience: ratings indicate satisfaction and so provide feedback to developers. User profiling: what types of actors use which resources? Trends in attention to different resources could be computed using social metadata date ranges.2010-09-27 LAK 12 19
  20. 20. Recommendations by clustering users or by building a social network graph and then recommending resources among a cluster or network. Feedback to developers about the utility of their resources, about who adapts them and how, and could eventually cause “widespread sharing” of learning resources to learners at the appropriate time.2010-09-27 LAK 12 20
  21. 21. +Learning Registry Ac kn www.learningregistry.org ow l ed ge m en ts Many slides courtesy of: Steve Midgley Office of Ed Tech US Department of Education & & Daniel R Rehak, PhD Susan Van Gundy ADL Technical Advisor Formerly UCAR/NSDL Department of Defense