c onnecting the dots to improve cyberlearning

Leyla Zhuhadar, Ph.D.• Research Scientist , Office of Distance

Learning, Western Kentucky University, USA.

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

Connecting the Dots to Improve Cyberlearning

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

• Phil Long and George Siemens, 2008 (Penetrating the Fog)

“Learning analytics refers to the interpretation of a wide range of data produced by and gathered on behalf of students in order to assess academic progress, predict future performance, and spot potential issues.”

• The 1st International Conference on Learning and Analytics & Knowledge, 2011, in Banff, Alberta

“Learning analytics is the measurement, collection, analysis and reporting of data about learners and their contexts, for purposes of understanding and optimizing learning and the environment in which it occurs”

• Buckingham & Ferguson, KMI, Social Learning Analytics, 2011

1) Social learning network analysis, 2) Social learning discourse analysis, 3) Social learning content analysis, 4) Social learning disposition analysis, 5) Social learning context analysis.

Background (Social Learning 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:

Cyberlearner and Open Source Platforms

The current search mechanism used by popular search engines to find LRs uses “Keywords Search” for retrieving isolated educational

resources to “Episodic Memory” – or knowledge based on a particular concept. The semantic search

could be considered as finding interrelated concepts – what we call “Semantic Memory.”

Cyberlearner would be able to grasp multiple concepts and build what we call

“Mental Encyclopedia.”

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.

What is the HyperManyMedia Repository?

Colliding Web Sciences

What is the HyperManyMedia Repository?

HMM Architecture

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

What is the HyperManyMedia 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 Speech and Visual Recognition

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

1% 3%7%

12%

5%

6%

8%

58%

Cyberlearners' Preferences (2006-2011)

Generic Search Metadata SearchSemantic Search Personalized Semantic SearchPersonalized Search with User Relevance Feedback Collaborative FilteringCross-Language Search Visual Search

> 750,000 Cyberlearners

We argue that mapping the hierarchical representation of

speech the way we visually categorize information can help

our Cyberlearners find what they are seeking!

An Analogy between Speech and Visual Recognition

Hierarchical Arrangement of:

An Analogy between Speech and Visual Recognition

Colleges (14)

Courses(60)

Resource(838)

Audio, Video, Lecture, RSS feed, Podcast, Vodcast, etc.

( >10,000)

• 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 Visual Recognition

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

What is the HyperManyMedia Repository?

Reminder: Connectingthe 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 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!

The Magic number of STM (7+/-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 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?

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?

• 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 (6 months logfile, 2011). ~8,000 Cyberlearners and ~24,000 (edges) connections among learners and resources in HMM.

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.

; where represents the weight of the edge between i and j, = is the sum of the weights of the edges attached to vertex i, is the community to which vertex i is assigned, the δ-function δ(u, v) is 1 if u = v and 0 otherwise and m =.

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.

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!

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.

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

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).

Short Term Memory for Chess Positions

Conclusions

• 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.

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.

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.

Finding Community!

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

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!

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 the Dots?

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 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 Cyberlearners

Thanks for your Attention!

Leyla Zhuhadar, Ph.D.Email: leyla.zhuhadar@wku.edu

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