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

Personalized Adaptive Web Systems 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 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 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 IMS LIP Similar to learner's CV Focus on Learner’s history and learning experience Lifelong model –Transfer between institution 11 categories –Identification: name, , … –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 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 Dolog LP By Dolog et al Uses RDF and learners’ ontologies –For personalization services 5 categories –Identification Name, telephone, address, , … –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 FOAF RDF vocabulary Properties and classes to describe –People, documents, and organizations For building communities and social groupings 5 categories –Basic information Name, , 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 Learner’s Features Taxonomy

Personalized Adaptive Web Systems Comparison of the Learner Models

Personalized Adaptive Web Systems 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 Comparison of the Learner Models

Personalized Adaptive Web Systems 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 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