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.