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Web services for supporting the interactions of learners in the social web - Roedunet 2010

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Web services for supporting the interactions of learners in the social web - Roedunet 2010

  1. 1. Web Services for Supporting the Interactions of Learners in the Social WebTraian Rebedea Mihai Dascalu Vlad Posea Stefan Trausan-Matu Faculty of Automatic Control and Computers University Politehnica of Bucharest
  2. 2. Contents• Context & Goals• Scenario• PolyCAFe – NLP Pipe and Advanced Discourse Techniques – Tagged LSA & Visualization – Utterance Evaluation and Collaboration – Participant Assessment and SNA• Social Network Assistant – Recommendation and Social Search Algorithms• Web Services and Widgets• Validation Results 2
  3. 3. Context• Education through dialogue• Technology – breaking space frontiers• Computer Supported Collaborative Learning - Chat & Forum -• Communities of Practice (CoP)• Social Web + Semantic perspective 3
  4. 4. Goals• Find knowledgeable peers• Automatically assess participants time• Support learners interacting in – Chats PolyCAFe – Forums – Social networking platforms Social Network Assistant 4
  5. 5. Scenario Topic of interest Social Network Assistant Peer students Engage in conversation Chats & Forums PolyCAFeAutomatic feedback and support 5
  6. 6. PolyCAFe architecture Feedback & Grading Collaboration Polyphony SNA Advanced NLP and discourse analysis Surface DomainNLP Pipe WordNet LSA Analysis Ontology 6
  7. 7. NLP Pipe & Advanced NLP and discourse analysis 1 • Stop-words elimination 2 • Spelling Correction 3 • Stemming 4 • Tokenizer 5 • Part of Speech Tagging• Speech acts identification• Co-references• Lexical chains derived from WordNet 7
  8. 8. Tagged LSA• Corpus of chats• Term-Doc Matrix + Tf – Idf• POS Tagging + Stemming• Segmentation k i 1 word 1,i , word 2,i Sim ( word 1 , word 2 ) – Participants k k i 1 word 12,i i 1 2 word 2,i – Fixed Window• Similarity Vector(utterance ) (1 log(no _ occurence wordi )) * vector(wordi ) ( i 1 Sim(utterance , utterance ) 1 2 Sim Vector(utterance ),Vector(utterance ) 1 2 8
  9. 9. Vector space visualization Radial ModelPhysical Model 9
  10. 10. Utterance evaluation & Collaboration • Degree Social • Betweenness • Semantic similarity Qualitative • Predefined topics • Discourse • NLP Pipe Quantitative • Occurrences• Social cohesion• Quantitative collaboration• Gain based collaboration Qualitative approach 10
  11. 11. Participant assessment and Social Networks Analysis• Multiple perspectives • Social Networks - – Quantitative metrics – Qualitative – Degree• Surface analysis – Centrality – Readability • Closeness • Flesch Reading Ease • Graph • Gunning’s Fog • Eigen Value • Flesch Grade Level – User Ranking – Page’s Essay Grading • Google Page Ranking • Fluency • Spelling • Diction • Utterance structure 11
  12. 12. Social Network Assistant Architecture • RDF Semantic Repository 12
  13. 13. Recommendation and Social Search Algorithms• Tag co-occurrence• Tag-resource affinities Tag Clustering• Tag-user affinities• FolkRank – Based on Google Page Rank – Tri-partite graph – Tag based attention profile 13
  14. 14. Web Services and Widgets• REST WS + W3C standard• Wookie widgets container• Possibly embedded in Web 2.0 & learning apps (Elgg, Moodle)• XML manipulation• Multiple functionalities & generated graphs 14
  15. 15. Validation Results• PolyCAFe formal validation – Student average (28/32 questions): 3,66 - 5,0 – Tutor average (35 questions): 3,5 - 5,0• Social Network Assistant – 90% score >= 4 usefulness – 70% social search more trustworthy than standard search• Timesaving for manual assessment ≈ 30%• Integration in formal learning context 15
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