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
SR
Funding 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.
Why worry about learning
resources?
2010-09-27 LAK '12 2

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 down
2010-09-27 LAK '12 3

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.
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
metadata
2010-09-27 LAK '12 5

6.
Data exhaust is wasted−it’s not
powering anything
Vs.
Data exhaust is collected to power
the social life of learning
resources
2010-09-27 LAK '12 6

7.
Learning resources languish on
the shelf
Vs.
Resources live in a rich
ecosystem
2010-09-27 LAK '12 7

8.
Data is locked in and locked down
Vs.
Democratization/Liberation of
data
2010-09-27 LAK '12 8

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.
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.
2010-09-27 LAK '12 11

12.
Annotational Paradata Kinematic Paradata Pedagogical Paradata
12
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…)
watched, interacted with, …
Group Collection
# 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
-ranked
Data exhaust is collected to power
LAK '12
the social life of learning resources

13.
Resources live in a rich ecosystem
2010-09-27 LAK '12 13

14.
+ Liberate the data…
2010-09-27 LAK '12 14

15.
+
Liberate the data…
2010-09-27 LAK '12 15

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.
Currently the Learning Registry
mainly contains metadata, published
+ from a number of collaborators, about
resource classification and usage data.
SHOW JIMS MAPPING HERE
2010-09-27 LAK '12 17

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 signature
2010-09-27 LAK '12 18

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.
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.
+
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