Leyla Zhuhadar, Rong Yang , Cyberlearners and Learning Resources, the Second International Conference on Learning Analytics and Knowledge (ACM): LAK12, Vancouver, British Columbia, Canada, 29 April – 2 May, 2012.

1. CYBERLEARNERS AND LEARNING
RESOURCES
Leyla Zhuhadar, PhD Rong Yang, PhD (Speaker)
- Assistant Professor, Department of
- Adj. Assistant Prof. CECS Dept., University of
Mathematics and Computer
Louisville, USA.
Science, Western Kentucky
- Research Scientist, WKU, USA.
University, USA.

2. THE MAIN THEMES
• How can we detect a community of similar
Cyberlearners based on the structure of a huge
social network?
• How can we present this interconnection
among communities visually to analyze our
Cyberlearners’ behaviors?
• Finally, building a community-based
recommendation system.
Social Learning Network Analysis (Buckingham & Ferguson, 2011)

3. CYBERLEARNER
AND
OPEN SOURCE PLATFORMS

4. HYPERMANYMEDIA
IS ALIGNED WITH THE
FOLLOWING IDEAS:
1. Technology enhanced learning: Open-source educational
resources (any place, any time, and in any way)
2. Using state of the art data mining algorithms and Web
services
3. Adopting a learner-centered pedagogical approach
4. Offering a mix of diverse content via Web 3.0.
5. Providing metadata, semantic, visualized, and cross-
language searchable content.
6. Recommender System
HyperManyMedia Repository: http://hmm.wku.edu

5. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

6. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

7. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

8. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

9. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

10. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

11. WHAT IS THE
HYPERMANYMEDIA
REPOSITORY?

12. CYBERLEARNERS’
PREFERENCES!
Cyberlearners' Preferences (2006-2011)
Generic Search Metadata Search
Semantic Search Personalized Semantic Search
Personalized Search with User Relevance Feedback Collaborative Filtering
Cross-Language Search Visual Search
1%
3%
7%
12%
5%
58%
6%
8%
> 750,000 Cyberlearners

13. HYPERMANYMEDIA
RESOURCES
• Hierarchical Arrangement of:
Colleges
(14)
Courses
(60)
Resource
(838)
Audio, Video, Lecture, RSS
feed, Podcast, Vodcast, etc.
( >10,000)

14. • This is great! But is our cognitive system
able to deal with this vast amount of
resources?
• The most difficult question raised here:
“Is our conceptual recognition of these
learning resources able to find what we
really want?

15. THE MAGIC NUMBER
OF SHORT TERM MEMORY (STM)
• In 1956, George Miller discovered the
magic number:
• 7 +/-2 = limited capacity of our Short
Term Memory
• Digital span, letter span, and visual
matrix

16. REMINDER:
I am a Cyberlearner and need help
to find a learning resource!
But, I really don’t know what type
of help I need!

17. LINKING SOCIAL NETWORKS WITH
RECOMMENDER SYSTEM: WHO ARE
MY NEIGHBORS?
 I am a Cyberlearner and need
help to find a resource!
But I really don’t know what type of
help I need!

18. THE MAGIC NUMBER
OF STM (7+/-2)
• Yes! We provided our Cyberlearners with a
semantic recommender system that gives
them related resources to their search; but
is this enough?
• Can I help our Cyberlearners to remember
these learning resources by linking/relating
them conceptually to other resources?

19. SEARCHING FOR
ANSWERS?
• But, how can we find this community with
common
• Learning domains,
• Problems,
• Interests, and
• Learning styles?
• Especially, when we have a system of
thousands of resources and hundreds of
thousands Cyberlearners navigating. We really
need help!

20. SEARCHING FOR
ANSWERS?
• Proposing a bottom-up approach (No pre-
knowledge).
• Data-driven approach: archived activities of
Weblogs for the last 6 years of Cyberlearners
visited HMM (~750,000).
• Looking underneath the structure of HMM
social networks.

21. This graph represents a social network structure of a weblog (2/1/2011-
8/1/2011). ~8,000 Cyberlearners and ~24,000 (edges) connections among
learners and resources in HMM.

22. FINDING
COMMUNITY!
• Network with High Complexity
• Small world (Kleinberg, 2000)
• Mine the structure to of the network to answer the
posed question
• Reminder! Simplistic approach
• Modularity measurement was used to visualize the
network structure.

23. FINDING
COMMUNITY!
• Discovering the community of Cyberlearners;
Each dot in this graph is a learner.
• 10 communities of learners with similarity
(commonality).
• Of course the distribution among the number
of dots ( Cyberlearners) varies; for the sake of
simplicity, we assume they are equally
distributed.

24. FINDING
COMMUNITY!
• If I am a Cyberlearner, I definitely belong to one
of these communities. Therefore, instead of
being a dot among 8,000 dots, I am now a dot
among 800 dots: Still it is a huge number 
• If I need a recommendation, I don’t want to
receive help from 800 Cyberlearners in my
community!

25. FINDING
COMMUNITY!
• Observing the graph (carefully):
• Each Cyberlearner has a unique distance from
the hub.
• A dot ahead is another learner (a little bit
more experienced with the resources in
this domain - closer the hub).
• A dot behind is a learner less experienced.
• A learner very close to the hub could be
considered an expert.

26. FINDING
COMMUNITY!
1. Do we want to intimidate a Cyberlearner
with an expert?
2. Or, do we provide the Cyberlearner with the
learner closest to him/her?
• distance-based = who has the most similar
profile to him/her

27. FINDING
COMMUNITY!
Our answer is neither one!
• We used another concept in cognitive
psychology—Chunking Hypothesis.
• In 1978, Herbert Simon introduced the
chunking hypothesis and won a Nobel
Prize in economics. "for his pioneering
research into the decision-making process
within economic organizations" (1978).

28. CONCLUSIONS
• Holding the concept of a primitive set
(Magic Number) and the concept of
chunking;
• Magic Number: Each Cyberlearner is
recommended with resources he/she did not
visit before from his/her closest 3 neighbors
(triangle); and
• Chunking: those recommendations should
range from 5 to 9 (no more).

29. This graph represents a social network structure of a weblog (May-
October 2011). ~8,000 Cyberlearners and ~24,000 (edges) connections
among learners and resources in HMM.

30. This graph represents a social network structure of a weblog (May-
October 2011). ~8,000 Cyberlearners and ~24,000 (edges) connections
among learners and resources in HMM.

31. This graph represents a social network structure of a weblog (May-
October 2011). ~8,000 Cyberlearners and ~24,000 (edges) connections
among learners and resources in HMM.

32. DID WE CONNECT
THE DOTS?
 I am a Cyberlearner and need
help to find a resource!
But I really don’t know what type
of help I need!

33. DID WE CONNECT
THE DOTS?
 I am a Cyberlearner and need
help to find a resource!
 But I really don’t know what type
of help I need!

34. HOW?

35. THE FUTURE OF CYBERLEARNERS
 Open, social learning
 Open Universities (Open Universities
(UK, Germany, India, etc.)
 Open Courseware (MIT, Khan Academy, etc.)
 Large open online courses ( Stanford: AI & ML)
 Social Learning Analytics
 Social learning network analysis
 Social learning discourse analysis
 Social learning content analysis
 Social learning disposition analysis
 Social learning context analysis

36. REFERENCES
1. Simon Buckingham Sum and Rebecca Ferguson, Social Learning
Analytics, Knowledge Media Institute, Social Learning Analytics, 2011.
2. George Miller (Magic Number, 1956)
3. Phil Long and George Siemens, Penetrating the Fog: Analytics in Learning
and Education, 2008.
4. Small-World Phenomena and Decentralized Search: Kleinberg. Navigation in
a Small World. Nature 406 (2000), 845.
5. Herbert Simon, The chunking
hypothesis, http://en.wikipedia.org/wiki/Herbert_Simon, 2005.
6. Mattieu Latapy, Main-memory triangle computations for very large (sparse
(power-law)) graphs, 2010.

37. THANKS FOR YOUR
ATTENTION!
Rong Yang, PhD. (speaker)
Email: rong.yang@wku.edu
&
Leyla Zhuhadar, Ph.D.
Email: leyla.zhuhadar@wku.edu