Personal recommender system for learners in learning networks

Abstract

This project is about the design and development of navigation services for distributed learning networks. Such services should recommend most suitable learning activities to learners regarding their personal needs and preferences. For this purpose we aim to develop a personal recommender system (PRS), that will use combinations of various prediction techniques. Learning networks can be filled with lots of learning activities stemming from different providers. Such networks are dynamic, because each member could add or delete content at any time. A personal recommender system is needed to support learners in selecting learning activities from a learning network that will enable them to achieve their (formal or informal) learning goal in a specific domain. It is expected that such support will minimize the amount of time learners need for finding suitable learning activities. The personal recommender system filters suitable learning activities regarding the needs and preferences of individual learners. A better alignment of the characteristics of learners and learning activities is expected to increase both effectiveness and efficiency of learning progress through the network