Abstract
Large amounts of information are nowadays easily obtainable using the Internet, and using implicit feedback whether a reader finds a text interesting is desirable. Eye-tracking technology could be used for such a feedback, and a combination of eye-movement features and a textual complexity measure can be used to predict the user's interest. In this paper we give an overview of a platform developed to evaluate and visualize implicit feedback of a person who reads a text. Based on the eye-movement samples provided, a model is trained that could be used to predict comprehensibility of a user reading a text. This prediction is combined with objective complexity evaluation of the text using data mining methods, and the outcome is used to select a text (from a repository) that a user may find more valuable (interesting). We briefly discuss the requirements, architecture and implementation of this platform.
Original language | English |
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Title of host publication | ECCE'18: Proceedings of the 36th European Conference on Cognitive Ergonomics |
Number of pages | 4 |
Volume | 36 |
Place of Publication | New York, NY, USA |
Publisher | Association for Computing Machinery |
Publication date | 5 Sept 2018 |
Article number | 13 |
ISBN (Print) | 978-1-4503-6449-2 |
DOIs | |
Publication status | Published - 5 Sept 2018 |
Event | European Conference on Cognitive Ergonomics - Utrecht, Netherlands Duration: 5 Sept 2018 → 7 Sept 2018 Conference number: 36 |
Conference
Conference | European Conference on Cognitive Ergonomics |
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Number | 36 |
Country/Territory | Netherlands |
City | Utrecht |
Period | 05/09/2018 → 07/09/2018 |
Series | ECCE'18 |
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