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

(Extended version of the paper published in the proceedings of the I-KNOW '04, Graz, Austria, 2004)
Christian Gütl 1,3 [ and], Maja Pivec 2,4 [ and], Christian Trummer 2 [], Victor Manuel García-Barrios 1,5 [ and], Felix Mödritscher 1,5 [ and], Juergen Pripfl 2 [], Martin Umgeher 2 []

English Abstract

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 fine-grained 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.

German Abstract

Der rasch ansteigende Umfang an Wissen bedingt einen zunehmenden Bedarf an effizienten und verbesserten Strategien von Wissenserwerb. E-Learning kann für unterschiedliche Lernaktivitäten in verschiedenen Lernumgebungen sehr hilfreich sein. Um jedoch verschiedene Lehr- und Lernmethoden zu unterstützen, muss E-Learning mehr als nur simples Lesen von Online-Lektionen bieten. Deshalb sollten E-Learning-Systeme Inhalte als auch Kommunikations- und Kollaborationsaktivitäten in stark personalisierter Weise unterstützten. Um ein geeignetes und feingranulares Benutzerprofil zu erhalten und um personalisierte Lerninhalte zur Verfügung zu stellen ist jedoch das Verfolgen und Erfassen vom Benutzerverhalten in Echtzeit eine große Herausforderung. In dieser Arbeit präsentieren wir AdeLE, eine Technologie-basierte Lösung eines verbesserten adaptiven E-Learning Frameworks, welches einen neuartigen Lösungsansatz für ein feingranulares Benutzerprofil durch Nutzung von Real-Time Eye-Tracking und Inhalts-Tracking Analysen sowie die Anwendung der dynamischen Hintergrundbibliothek umfasst. Aufbauend auf den allgemeinen Zielen der verbesserten E-Learning Umgebung werden die AdeLE Systemarchitektur und die Methoden zur Gewinnung von feingranularen Benutzerinformationen mittels Real-Time Eye-Tracking adressiert. Weiters werden verschieden Anwendungsszenarien in unterschiedlichen Bereichen erläuternd dargestellt.

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e-learning, distance learning, distance education, online learning, higher education, DE, blended learning, MOOCs, ICT, information and communication technology, collaborative learning, internet, interaction, learning management system, LMS,

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