Using Eye Tracking as Human Computer Interaction Interface

  • Holger Schmidt
  • Gottfried Zimmermann
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 528)


In the project AAMS, we have developed the e-learning platform ALM for Ilias as a technical basis for research in education. ALM uses eye tracking data to analyze a learner’s gaze movement at runtime in order to adapt the learning content. As an extension to the actual capabilities of the platform, we plan to implement and evaluate a framework for advanced eye tracking analysis techniques. This framework will focus on two main concepts. The first concept allows for real-time analysis of a user’s text reading status by artificial intelligence techniques, at any point in the learning process. This extends and enriches the adaptive behavior of our platform. The second concept is an interface framework for multimedia applications to connect to any eye tracking hardware that is available at runtime to be used as a user interaction input device. Since accuracy can be an issue for low-cost eye trackers, we use an object-specific relevance factor for the detection of selectable or related content.


Eye tracking e-learning platform Adaptivity Artificial intelligence High-level gaze events Real-time analysis Human computer interaction interface Relevance factor 


  1. 1.
    Science Campus Tübingen, campus website, April 2015.
  2. 2.
    Ilias 4 Open-Source Framework, project website, April 2015.
  3. 3.
    Schmidt, H., Wassermann, B., Zimmermann, G.: An adaptive and adaptable learning platform with real-time eye-tracking support: lessons learned. In: Trahasch, S., Plötzner, R., Schneider, G., Gayer, C., Sassiat, D., Wöhrle, N (eds.), Tagungsband DeLFI 2014, pp. 241–252. Köllen Druck & Verlag GmbH, Bonn (2014)Google Scholar
  4. 4.
    Wassermann, B., Hardt, A., Zimmermann, G.: Generic Gaze Interaction events for web browsers: using the eye tracker as input device. In: WWW 2012 Workshop: Emerging Web Technologies, Facing the Future of Education (2012)Google Scholar
  5. 5.
    Schubert, C., Scheiter, K., Schüler, A.: Viewing behavior during multimedia learning: can eye tracking measures predict learning success? In: 7th European Conference on Eye Movement Research. Lund, Sweden, August 2013Google Scholar
  6. 6.
    Geoffrey, B.D., Payne, S.J.: How much do we understand when skim reading? In: CHI 2006 Extended Abstracts on Human Factors in Computing Systems (CHI EA 2006), pp. 730–735. ACM, New York (2008). doi: 10.1145/1125451.1125598

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Stuttgart Media UniversityStuttgartGermany

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