Automatic Personalization in E-Learning Based on Recommendation Systems
Mohamed Koutheaïr Khribi, Mohamed Jemni, Olfa Nasraoui · IGI Global eBooks · 2011
Web based learning environments are being increasingly used at a large scale in the education area. This situation has brought a dramatic growth in the amount of educational resources and services incorporated continuously in these systems, and related access and usage of this educational content by a diversity of learners. However, the delivery of this educational content is generally done in the same way for all learners without giving any special attention to the different consumption styles or differences between their profiles and individual needs. Therefore, providing personalization in e-learning systems has to be considered as a necessity and not an option. Recommending suitable links represents an instance of adaptive navigation support technology. E-learning recommender systems are used to locate relevant educational Web objects that better match the learner’s profile and interests, this requires the ability of a system to predict learner’s needs and preferences. Therefore, recommendation systems need to use Web mining techniques in one or more phases of the recommendation process, especially in the modelling and pattern discovery phase. Most emergent recommendation systems in e-learning tend to rely on automated detection of student’s preferences and needs since it is more efficient and attractive to provide needed support to students without requesting any explicit information from them. In this chapter, we present an overview of personalization in e-learning based on recommendation systems and Web mining techniques.