Modeling parallel web browsing behavior for web-based educational systems

When learners access web-based educational systems, they often take advantage of what current web browsers offer: multiple tabs with different pages. Whether they are solving an exercise or learning a whole new topic, they can concurrently browse other relevant objects in the educational system or even resources outside the system, on the “wild” Web. This behavior goes unnoticed or it is only estimated in the traditional web usage mining, where it is supposed that the user opens only one page at a time. However, if we could record the parallel browsing behavior, we could better understand the user's activity, goals and improve learner model employed for personalization. The paths through resources (including spawning new tabs and switching between them) of many learners also express relevance between these resources and this can be leveraged in applications such as recommendation to other learners browsing similar learning objects. In this paper, we introduce a model for parallel browsing behavior based on events tracked with client-side scripting. We realized the proposed model in the ALEF educational system, which we use in several courses and evaluate learner behavior in the system.