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C onnecting the Dots to Improve Cyberlearning

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. Prepared for NSF Cyberlearning

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C onnecting the Dots to Improve Cyberlearning

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  1. Connecting the Dots to Improve Cyberlearning LeylaZhuhadar, 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 Research Summit in Washington, D.C. January 18, 2012.

  2. Background (Social Learning Analytics): • 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.

  3. The Main Themes: • 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.

  4. Cyberlearner and Open Source Platforms

  5. The current search mechanism used by popular search engines to find LRs use “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.”

  6. HyperManyMediais aligned with the following ideas: • Technology enhanced learning: Open-source educational resources (any place, any time, and in any way) • Using state of the art data mining algorithms and Web services • Adopting a learner-centered pedagogical approach • Offering a mix of diverse content via Web 3.0. • Providing metadata, semantic, visualized, and cross-language searchable content.

  7. What is the HyperManyMediaRepository? Colliding Web Sciences

  8. What is the HyperManyMediaRepository? HMM Architecture

  9. What is the HyperManyMediaRepository?

  10. What is the HyperManyMediaRepository?

  11. What is the HyperManyMediaRepository?

  12. What is the HyperManyMediaRepository?

  13. What is the HyperManyMediaRepository?

  14. What is the HyperManyMediaRepository?

  15. What is the HyperManyMediaRepository?

  16. 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?

  17. An Analogy between Speechand Visual Recognition • “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 55phonemes to represent virtually all the words in all the languages spoken around the world • (Biederman, 1987).”

  18. What is the HyperManyMediaRepository?

  19. What is the HyperManyMediaRepository? > 750,000 Cyberlearners

  20. An Analogy between Speech and Visual Recognition • We argue that mapping the hierarchical representation of speech the way we visually categorize informationcan help our Cyberlearnersfind what they are seeking!

  21. An Analogy between Speech and Visual Recognition • Hierarchical Arrangement of:

  22. The Power of Visual Recognition • We call this representation “top-down” cognitive representation. • It starts with a knowledge driven by the Cyberlearnerwho knows what he/she is looking for. • Visually finds his/her learning resource with three clicks!

  23. What is the HyperManyMediaRepository?

  24. What is the HyperManyMediaRepository?

  25. What is the HyperManyMediaRepository?

  26. 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!

  27. 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!

  28. 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

  29. 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?

  30. 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!

  31. 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.

  32. 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.

  33. 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 =.

  34. 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.

  35. 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!

  36. 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.

  37. Finding Community! • Do we want to intimidate a Cyberlearnerwith an expert? • Or, do we provide the Cyberlearner with the learner closest to him/her? • distance-based = who has the most similar profile to him/her

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

  39. Short Term Memory for Chess Positions

  40. 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).

  41. 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.

  42. 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.

  43. 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.

  44. Finding Community! • Ironically, the concept of triangles (triads) has proved to have the same properties of small world [MatthieuLaptapy, 2010]

  45. Did We Connect the Dots? I am a Cyberlearnerand need help to find a resource! But I really don’t know what type of help I need!

  46. Did We Connect the Dots? I am a Cyberlearnerand need help to find a resource! But I really don’t know what type of help I need!

  47. How?

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

  49. 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

  50. Thanks for your Attention! LeylaZhuhadar, Ph.D. Email: leyla.zhuhadar@wku.edu

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