Presented for the SolaR distributed Doctoral Research Lab (www.distributedlab.net)

1. +
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The Learning
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Registry: Building a
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Bi
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Foundation for
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Learning Resource
al
Analytics
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tio
na
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SR
Funding from
US Dept of Education
US Dept of Defense
Numerous contributions from the
Learning Registry Open Source
Community. Special thanks to
NSDL, JISC, and Navigation North

2. Why worry about learning
resources?
2013-04-04

3. Fading Education Practices
• Hour-long classes, “seat time”
requirements
• Students grouped by age
• Lecture-based teaching
• Paper textbook as primary
learning resource; No cell
phones in class
• Small, delayed and
disconnected data: some
testing feedback, reports
(midterm, final), attendance,
free lunch eligible

4. Deeply Digital Learning
• Flipped classroom w/online
practice and homework via
adaptive tutors
• More engaging and inspiring
24/7 learning: games, projects,
badges for competencies
• Learners collaborate by ability,
interest
• Digital media/platforms for
open or personalized learning
• Data ecosystems including the
Internet of Learning Things

5. Analytics and Data Mining
• Continuously improving courses, curricula, and apps
• Continuous and stealth
testing
• Personalized, adaptive
learning pathways;
recommended online
learning resources
• Students succeed with the
right challenge, right
encouragement, and right
engagement
• Interactive data
visualization systems (aka
“dashboard”) for learners,
teachers, leaders

6. 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 down

7. Stovepiped repositories hold data
and share via pipes...
Vs.
Data is shared in the open for
aggregation, amplification,
analysis…

8. 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 pool with social
usage data (attention metadata,
paradata) to add nuance to
metadata

9. Data exhaust is wasted−it’s not
powering anything
Vs.
Data exhaust is collected to power
the social life of learning
resources

10. Learning resources languish on
the shelf
Vs.
Resources live in a rich
ecosystem

11. Data is locked in and locked down
Vs.
Democratization/Liberation of
data

12. 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.

13. 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…

14. LAK '12

15. Annotational Paradata Kinematic Paradata Pedagogical Paradata
15
Refines descriptive Illustrates diffusion through Refines educational context
metadata user actions and utility
Tagged (as…) Clicked User demographics aggregated
frequency by tag accessed, # of hits, … by paradata contributor
Recommended / Included in Viewed Embedded (in…)
Group Collection watched, interacted with, …
# of times
Correlated / Aligned (to…)
Downloaded to standard, to grade level, to curriculum
Commented / Discussed saved a local copy
# of times – content of comments/discussions
handled as annotations
Favorited / Included in Modified
reused, created derivative work, added to,
Personal Collection contextualized, personalized, enhanced,
Rated foldered, listed, bookmarked, favorited, combined
avg rating by community, star rating, usability playlisted
rating, … =====================
-implemented (in context…)
Shared to Social Media
Voted 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 Data exhaust is collected to power
-ranked
the social life of learning resources

16. Resources live in a rich ecosystem
2010-09-27 LAK '12

17. + Liberate the data…
2010-09-27 LAK '12

18. +
LAK '12
Liberate the data…

19. 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.
LAK '12

20. Currently the Learning Registry
mainly contains metadata, published
+ from a number of collaborators, about
resource classification and usage data.
SHOW JIMS MAPPING HERE

21. 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 signature

22. Paradata Schema
-- actor (required, except for assertions): the person or group who
does some-thing; a string or a compound object (as shown below) that
describes characteristics of the actor
object-type: the actor value
description: information about the actor
-- verb (required): the action that is taken; it can be a simple string. If
it is a compound object, then verb contains:
action: the verb value
measure: the occurrences of the verb (whether counts,
averages, ratings, or other things).
date (optional): start time / stop time when the action occurs.
context (optional): place where the action takes place
-- object (optional): the thing being acted upon. The important part of
an object is the URL (or URI) where you can find out about the object
(or get the object, or ideally both).
-- related (optional): describes a relationship between the object
(above) and other objects listed within
-- content (optional): a human-readable string description of what the
paradata item expresses

23. Data
Services

24. Some Active Adopters/Experimenters
Naviation North: Brokers of Expertise and CTE
Online http://www.commoncorelabs.com
inBloom K-12 Student Integration Systems:
https://www.inbloom.org/services
JLeRN Experiment:
http://jlernexperiment.wordpress.com
State Improvement Systems

25. 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.

28. What’s Next?
Work on identity, creating the “network effect,”
digital signing for spam control, fear of loss of
competitive advantage from data sharing
Gathering performance statistics
LRMI validation (tip the preference to LRMI)
Accessibility metadata
https://wiki.benetech.org/display/a11ymetadata/

29. References
Bienkowski and Klo. Forthcoming. The Learning Registry:
Applying Social Metadata for Learning Resource
Recommendations. in Nikos Manouselis, Katrien Verbert,
Hendrik Drachsler and Olga C. Santos, Eds. Recommender
Systems for Technology Enhanced Learning: Research Trends &
Applications, Heidelberg: Springer
Lee A, Hobson, J. Bienkowski, M. Midgley, S. Currier, S
Campbell L M, Novoseova, T. 2012. Towards Networked
Knowledge: The Learning Registry, an Infrastructure for Sharing
Online Learning Resources. Educational Technology. V. 52, N. 6.
November-December. pp 14-19.
Bienkowski, M., Brecht, J., & Klo, J. (2012). The Learning
Registry: Building a Foundation for Learning Resource Analytics.
Learning Analytics and Knowledge 2012 (LAK'12): 29 April – 2
May 2012, Vancouver, BC, Canada.
https://github.com/LearningRegistry/LearningRegistry

30. +
Learning Registry
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Kl Mai
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Many slides courtesy of: t G te
Steve Midgley ra rs:
ta Jim
Office of Ed Tech ,A
DL
US Department of Education Susan Van Gundy
& Formerly UCAR/NSDL
Daniel R Rehak, PhD
(former) ADL Technical Advisor
Department of Defense