1. Solutions for Personalized T-learning Alberto Gil Solla Department of Telematic Engineering University of Vigo (Spain) EuroITV 2005: the 3rd European Conference on Interactive Television Aalborg, Denmark April 1, 2005

2. Introduction Migration from analogue to digital TV   Interactive multimedia applications mixed with audiovisual contents   Standards to normalize receivers (MHP) T-learning is gaining popularity:   Life-long education is essential in the current global economy   Engaging applications for digital TV are needed

3. T-learning vs e-learning T-learning is different from e-learning   E-learning involves active users   TV traditional passive attitude demands an edutainment approach A likely overwhelming increase in T-learning contents will disorient users   Tools will be needed to assist them to find interesting personalized educational material

4. AVATAR AdVAnced Telematic search of Audiovisual contents by semantic Reasoning Framework to test recommendation strategies:   Profiles matching (collaborative filtering)   Semantic reasoning about the user preferences and TV programs (enhanced content-based techniques)   The knowledge base is an OWL ontology about the TV domain, describing hierarchies of classes and properties. Specific instances are extracted from TV-Anytime program descriptions Extended to applying the same techniques to recommend personalized T-learning contents

5. User profile Watching habits Learning history Courses Contents Descriptive metadata Recommender agent TV-Anytime LOM LIP

6. IEEE LOM: Learning Object Metadata IEEE standard to describe educational material: contents, purposes, formats, level of difficulty, languages, authors, intended audience, dependencies... Enables search and discovery of contents Enhancements for t-learning needed

7. TV-Anytime To describe: TV programs Content segmentation Users’ personal profiles Users’ viewing habits

8. LIP: Learner Information Package IMS standard to describe any relevant information about the user Combined with LOM, it permits making access to a course dependent on having proved some knowledge

9. Conclusions Applying semantic web techniques can improve course targeting, so optimising advertisement investments Standards are essential