Leyla Zhuhadar, Ph.D. Research Scientist, Office of Distance Learning, Western Kentucky University, USA. Adjunct Assistant Prof. CECS Dept., University

Leyla Zhuhadar, Ph.D.

•Research Scientist, Office of Distance Learning, Western Kentucky University, USA.

•Adjunct Assistant Prof. CECS Dept., University of Louisville, USA.

Prepared for NSF Cyberlearning Prepared for NSF Cyberlearning Research Summit in Washington, D.C. Research Summit in Washington, D.C. January 18, 2012. January 18, 2012.

Background (Social Learning Background (Social Learning Analytics):Analytics):

• Social Learning Content Analysis (Buckingham & Ferguson)• How can an overwhelming amount of information be easily

presented when it is stored in conceptual visualized matter?

• Why it is important to mimic the sequential extraction of information occurring in ecological vision (“top-down” cognitive representation) rather than using a holistic approach?

• Social Learning Netwotk Analysis (Buckingham & Ferguson)• 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.

The Main Themes:The Main Themes:

Cyberlearner Cyberlearner and and Open Source PlatformsOpen Source Platforms

The knowledge retained by Cyberlearners when they use popular search engines (keywords search) to find learning

resources could be compared to Episodic Memory

– or knowledge based on a particular concept.

The semantic search not only increases the knowledge base on a particular concept, it also helps the searcher discover other

words, notions, or ideas related to the original item searched–what we call

Semantic Memory. Our goal is to help Cyberlearners to grasp multiple concepts

and build–what we call Mental Encyclopedia.

HyperManyMedia HyperManyMedia iis 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.

What is the What is the HyperManyMedia HyperManyMedia Repository?Repository?

Colliding Web SciencesColliding Web Sciences

What is the What is the HyperManyMediaHyperManyMedia Repository?Repository?

HMM ArchitectureHMM Architecture

What is the What is the HyperManyMedia HyperManyMedia Repository?Repository?







• 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?

“Lexical access during speech perception can be successfully modeled as a process mediated by

identification of individual primitive elements, the phonemes from a small set of primitives (Wilsom,

1980).

We need only 44 phonemes to code all the words in English and 55 phonemes to represent virtually all the words in all the languages spoken around the

world

(Biederman, 1987).”

An Analogy between An Analogy between SpeechSpeech and and Visual RecognitionVisual Recognition



> 750,000 Cyberlearners

We argue that mapping the We argue that mapping the hierarchical representation of hierarchical representation of

speech the way we visually speech the way we visually categorize information can help categorize information can help

our Cyberlearners find what they our Cyberlearners find what they are seeking!are seeking!


Hierarchical Arrangement of:


• We call this representation “top-down” cognitive representation.

• It starts with a knowledge driven by the Cyberlearner who knows what he/she is looking for.

• Visually finds his/her learning resource with three clicks!

The Power of The Power of Visual Visual RecognitionRecognition




Reminder: Reminder: ConnectingConnectingthe Dots!the Dots!I am a Cyberlearner and need help to find a learning resource!

But, I really don’t know what type of help I need!

Linking Social Networks with Linking Social Networks with Recommender Recommender System: System: Who Are my Neighbors?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!

The Magic number The Magic number of STM (of STM (7+/-27+/-2))

In 1956, George Miller discovered the magic number:

• 7 +/-2 = limited capacity of our Short Term Memory

•Digital span, letter span, and visual matrix

The Magic number The Magic number of STM (of STM (7+/-27+/-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?

Searching for Answers?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!

Searching for Answers?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.

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.

Finding Community!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 (analogy between language and perception still holds)

• Modularity measurement was used to visualize the network structure.

Finding Community!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.


• 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!

Finding Community!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.


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


Our answer is neither one!Our answer is neither one!

•We used another concept in We used another concept in cognitive psychology—cognitive psychology—Chunking Chunking HypothesisHypothesis. .

•In 1978, Herbert Simon introduced In 1978, Herbert Simon introduced the chunking hypothesisthe chunking hypothesis and won a and won a Nobel Prize in economics. "for his Nobel Prize in economics. "for his pioneering research into the decision-pioneering research into the decision-making process within economic making process within economic organizations" (1978).organizations" (1978).

Short Term Memory for Chess Positions

ConclusionsConclusions• Holding the concept of a primitive set

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).

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.




Ironically, the concept of triangles (triads) has proved to have the same properties of small world [Matthieu Laptapy, 2010]

Did We Connect Did We Connect the Dots?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!

I am a Cyberlearner and need help to find a resource!

But I really don’t know what type

of help I need!

Did We Connect Did We Connect the Dots?the Dots?

How?How?

Open, social learning Open Universities (UK, Germany, India, etc.) Open Courseware (MIT, Khan Academy, etc.) Large open online courses (Stanford: AI & ML)

The Future of The Future of Cyberlearners 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

The Future of The Future of Cyberlearners Cyberlearners

Thanks for your Attention!

Leyla Zhuhadar, Ph.D.Email: [email protected]

1. Simon Buckingham Sum and Rebecca Ferguson, Social Learning Analytics, Knowledge Media Institute, Social Learning Analytics, 2011.

2. Phil Long and George Siemens, Penetrating the Fog: Analytics in Learning and Education, 2008.

3. Small-World Phenomena and Decentralized Search: Kleinberg. Navigation in a Small World. Nature 406 (2000), 845.

4. Herbert Simon, The chunking hypothesis, http://en.wikipedia.org/wiki/Herbert_Simon, 2005.

5. Mattieu Latapy, Main-memory triangle computations for very large (sparse (power-law)) graphs, 2010.

References

Documents

Leyla Zhuhadar, Ph.D. Research Scientist, Office of Distance Learning, Western Kentucky University, USA. Adjunct Assistant Prof. CECS Dept., University