Support Personalization in Distributed E-Learning Systems through Learner Modeling

The growing popularity of e-learning has created a need for personalization. Based on individual difference of learners´ abilities and preferred learning styles in hypermedia environment, the learning outcomes vary essentially. Meanwhile, with the development of e-learning technologies, learners can be provided more effective learning environment to optimize their learning. Adaptive e-learning systems are built to personalize and adapt e-learning content, pedagogical models, and interactions between participants in the environment to meet the individual needs and preferences of users if and when they arise. The learner model is an essential component in adaptive e-learning systems since it is used to modify the interaction between system and learners to suit the needs of individual learners. While learner model plays an important role in the personalization of e-learning systems, it is seen as a part of the e-learning system in many applications. In our paper, we first explain the reason of why this issue prevents the e-learning systems to provide better personalized support to meet the individual learning requirements. Then we propose a novel centralized learner model technique for e-learning systems to overcome those problems. To achieve the goal to design a learner model for distributed systems meeting the requirements, the paper introduce five work steps: analyze e-learning standards, articulate modeling requirements, develop the representation of learning styles, generate learner activity ontology and design the learner model. Finally, we illustrate the architectural design of modeling system