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Towards a Semantic Modeling of Learners for Social Networks

Towards a Semantic Modeling of Learners for Social Networks. Asma Ounnas, ILaria Liccardi, Hugh Davis, David Millard, and Su White Learning Technology Group University of Southampton, UK Presented by Rosta Farzan Personalized Adaptive Web Systems Lab. Introduction.

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Towards a Semantic Modeling of Learners for Social Networks

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  1. Towards a Semantic Modeling of Learners for Social Networks Asma Ounnas, ILaria Liccardi, Hugh Davis, David Millard, and Su White Learning Technology Group University of Southampton, UK Presented by Rosta Farzan Personalized Adaptive Web Systems Lab

  2. Introduction • Social networks is important in distant learning • Physically different location and different life • Need friends who share same interests, preferences, and learning experiences • Learner model • Building social networks of learners • This work • An extension of Friend of a Friend (FOAF) ontology to build learner model for social networks Personalized Adaptive Web Systems

  3. Outline • Existing learner models • Learner’s feature taxonomy • Comparison of the learners model • Extension of FOAF as a learner model • Conclusion & Future Work Personalized Adaptive Web Systems

  4. PAPI • IEE LTSC • Data interchange specification • Describes learner information for communication among cooperating systems • Personal information • General information e.g. name, address, … • 6 Categories • Relations information • Learners’ relationships with others e.g. classmate • Security information • Access rights • Preference information • Public information about the learner’s preferences e.g. learning style, language, … • Performance information • Records of learner’s measure performance e.g. grades • Portfolio information • Learner’s projects and works Personalized Adaptive Web Systems

  5. IMS LIP • Similar to learner's CV • Focus on Learner’s history and learning experience • Lifelong model • Transfer between institution • 11 categories • Identification: name, e-mail, … • Goal: Learning, Career, … • Qualification, Certification, License • From recognized authorities • Activity: learning activities in any state of completion • Interest: hobbies and recreational • Relationship: between core data elements • Competency: skills and experiences • Accessibility: language capabilities, learning preferences, disabilities • Transcript: official academic achievements • Affiliation: organization • Security Key: password Personalized Adaptive Web Systems

  6. eduPerson • By Internet2 and Educause • Facilitate communication between higher education institution • Similar to employee information system • Detailed description • 43 elements in 2 categories • General attributes • Information about the learner, the organization, and references • New attributes • To facilitate collaboration between the institution • E.g. Affiliation, ID for authentication, … Personalized Adaptive Web Systems

  7. Dolog LP • By Dolog et al • Uses RDF and learners’ ontologies • For personalization services • 5 categories • Identification • Name, telephone, address, email, … • Other user features • Preferences, Goal, and Interests • Study performance • Performance, portfolio, and certification • Human resource planning • Organization • Calendar • Appointments and events Personalized Adaptive Web Systems

  8. FOAF • RDF vocabulary • Properties and classes to describe • People, documents, and organizations • For building communities and social groupings • 5 categories • Basic information • Name, email, images, homepage • Personal information • Weblogs, interests, publications • Online accounts • Projects and groups • Projects, organizations • Documents and images • E.g. personal profile document, logo Personalized Adaptive Web Systems

  9. Learner’s Features Taxonomy Personalized Adaptive Web Systems

  10. Comparison of the Learner Models Personalized Adaptive Web Systems

  11. Comparison of the Learner Models • PAPI, LMS LIP, and Dolog PL • Best for adaptive e-learning • eduPerson • Collecting data and transferring between institution • FOAF • Automatic personalization • Describes learner’s relations with others by pointing to learner “knows” Personalized Adaptive Web Systems

  12. Comparison of the Learner Models Personalized Adaptive Web Systems

  13. Extending FOAF • Advantages of FOAF • RDF • 1.5 millions FOAF documents • FOAF vocabularies evolves • FOAF files are easy to create • Facilitates locating people with similar interest • Security and privacy issues are taken care Personalized Adaptive Web Systems

  14. Extending FOAF • Required feature for using FOAF as a learner model • Personal Data • Spoken and written language, gender, learning styles, preferred modules • Relations • Taking courses, taking module, … • Evaluating strength of the relationships between learners • Algorithm for building social networks of learners Personalized Adaptive Web Systems

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