Adapting e-learning contents based on navigation styles and preferences

Personalized access to e-learning contents is based on suggesting routes to users considering their preferences. Recent studies show that navigation styles can content much of the information related to cognitive learning styles and preferences. These navigation styles can be defined by means of different parameters. In this work, we focus on the parameter that defines if the navigation style is either local or global. Local refers to a navigation style where the student prefers to learn one aspect of the topic in depth before going on. This type of student does not want to be distracted with irrelevant information, whereas students with Global navigation style want to have a broad idea before starting with details. To measure these navigation styles, deviations over a proposed initial learning route are taken into account. The more Local properties found in the navigation, the smaller the number of deviations. If the number of deviations is high, students are supposed to have a Global Navigation style, where the proposed route is exceeded. This parameter (Local/Global) is then analyzed against a more complete student learning profile.