AdeLE (Adaptive e-Learning with Eye-Tracking): Theoretical Background, System Architecture and Application Scenarios

Due to the rapidly growing amount of knowledge, a stronger need emerges for efficient and improved knowledge acquisition strategies. E-learning can be very helpful for different learning activities in various learning environments. However, in order to support different teaching and learning paradigms, e-learning should deal with more than simply reading online lessons. Therefore, content as well as communication and collaboration have to be supported in a highly personalised manner by e-learning systems. Though, tracking and grasping the user behaviour in real time remains the most challenging task to retrieve an appropriate and finegrained user profile as well as to provide personalised learning content. In this paper we present AdeLE, a technology-based solution of an enhanced adaptive e-learning framework, which comprises novel solution approaches for fine-grained user profiles by exploiting real time eye-tracking and content-tracking analysis as well as a dynamic background library. Based on the global objectives of an enhanced e-learning environment, the system architecture of AdeLE and the methods used in order to gain fine-grained user information by real time eye-tracking are addressed. Furthermore, various scenarios in different application domains are illustrated.