The case for dynamic difficulty adjustment in games
Proceedings of the 2005 ACM SIGCHI International Conference on Advances in computer entertainment technology
A robust realtime reading-skimming classifier
Proceedings of the Symposium on Eye Tracking Research and Applications
Reading and estimating gaze on smart phones
Proceedings of the Symposium on Eye Tracking Research and Applications
I know what you are reading: recognition of document types using mobile eye tracking
Proceedings of the 2013 International Symposium on Wearable Computers
Recognition of understanding level and language skill using measurements of reading behavior
Proceedings of the 19th international conference on Intelligent User Interfaces
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An increasing amount of text is being read digitally. In this paper we explore how eye tracking devices can be used to aggregate reading data of many readers in order to provide authors and editors with objective and implicitly gathered quality feedback. We present a robust way to jointly evaluate the gaze data of multiple readers, with respect to various reading-related features. We conducted an experiment in which a group of high school students composed essays subsequently read and rated by a group of seven other students. Analyzing the recorded data, we find that the amount of regression targets, the reading-to-skimming ratio, reading speed and reading count are the most discriminative features to distinguish very comprehensible from barely comprehensible text passages. By employing machine learning techniques, we are able to classify the comprehensibility of text automatically with an overall accuracy of 62%.