Toward a Fully Automatic Learner Modeling Based on Web Usage Mining with Respect to Educational Preferences and Learning Styles

In this paper, we describe a fully automatic learner modeling approach in learning management systems, taking into account learners' educational preferences including learning styles. We propose a learner model with three components: the learner's profile, learner's knowledge, and learner's educational preferences. The learner's profile represents the learner's general information such as identification data, the learner's knowledge implies the learner's interests on visited learning objects, and the learner's educational preferences are composed of the learner's preferences among visited learning objects and his/her learning style. In the proposed approach, all learner model components are automatically detected, without requiring explicit feedback. Indeed, all the basic learners' information is inferred from the learners' online activities and usage data, based on web usage mining techniques and a literature-based approach for the automatic detection of learning styles in learning management systems. Once learner models are built, we apply a hierarchical multi-level model based collaborative filtering approach, in order to gather learners with similar preferences and interests in the same groups.