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Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data
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Multiple Methods and Techniques in Analyzing Computer-Supported Collaborative Learning (CSCL) Data

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5 March 2010 (Friday) | 09:00 - 12:30 | http://citers2010.cite.hku.hk/abstract/69 | Dr. Yanyan LI, School of Educational Technology, R&D Center for Knowledge Engineering, Beijing Normal University

5 March 2010 (Friday) | 09:00 - 12:30 | http://citers2010.cite.hku.hk/abstract/69 | Dr. Yanyan LI, School of Educational Technology, R&D Center for Knowledge Engineering, Beijing Normal University

Published in: Education, Technology
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  • 1. Toward a Semantic Forum for Active Collaborative Learning<br />Yanyan Li<br />Knowledge Science & Engineering Institute, <br />School of Education Technology,<br />Beijing Normal University<br />liyy1114@gmail.com<br />
  • 2. Problems of Existing Forums<br /><ul><li>The messages being delivered are arbitrarily structured and the content is dynamically generated through multiple learners’ inputs and interactions, thus learners have to spend much time to go through nonlinear messages for locating useful messages.
  • 3. Messages are incoherent and disordered without semantic association. So, learners cannot search for the specific topic by retrieving certain discussion transcripts in separation.
  • 4. Learners usually feel isolated and are easily tended to become disengaged and inactive when they are outside of the social context of the classroom</li></li></ul><li>Goal<br />Design a semantic forum with facilities such as semantic search, relational navigation and recommendation to better support informal learning.<br />Messages are processed and re-organized with semantic association <br />Special interest groups are automatically discovered for topic-centric social context measurement.<br />
  • 5.
  • 6. Processing Discussion Transcripts for Semantic Organization<br />Message Topic Recognition<br />Message Type Identification<br />Semantic Association of Messages<br />
  • 7. Message topic recognition<br />Assuming that the messages in a discussion thread represent the same topic, we combine them into a summary document. Each document is represented as a weighted term vector d = (d1, d2,…) withthestandard TFIDF function. The text in title field and body field of messages are treated discriminatively.<br />The cosine method is adopted to compute the similarity between the document vector and the concept vector in the knowledge map.<br />
  • 8. Message type identification <br />
  • 9. Semantic Association of Messages<br />Semantic relationship, such as Sequential, Part-of, Similar-to, Cause-effect, Contrast, Build-on, etc. <br />
  • 10. Topic-centric Social Context Measurement by Discovering SIGs<br />Combining SNA with semantic relations for constructing SIGs<br />
  • 11. Compute whether a learner belongs to a SIG or not<br />Energy<br />Compute several criteria for a SIG member<br />Participation<br />Mutuality<br />Activity<br />
  • 12. Supporting Intelligent Facilities<br />A schematic navigation view<br />
  • 13. Recommendation<br />Best-messages view, allowing learners to browse the messages that are set as good by the host.<br />Popular-messages view, enabling learners to browse the messages that draw a lot of attention from others by ranking the messages according to the number of replying and clicking.<br />Messages-type view, empowering learners to quickly locate the messages with specific types.<br />Peer-centric message view, enabling learners to know about all the messages delivered by a peer. <br />
  • 14. Experimental Study<br />W3CHINA discussion board (available at http://bbs.w3china.org/index.asp) as the data source. We randomly selected 763 discussion threads in a view of “Semantic Web and ontology”, with a total of 4512 messages from the source.<br />
  • 15. Original relationship network on “DL”<br />Discovered SIG on “DL”<br />
  • 16. Original relationship network on “ontology”<br />Discovered SIG on “ontology”<br />
  • 17. Thanks!<br />Welcome Questions and Comments<br />

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