Platform for Evaluation of Readers' Implicit Feedback Using Eye-Tracking

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 languageEnglish
Title of host publicationECCE'18: Proceedings of the 36th European Conference on Cognitive Ergonomics
Number of pages4
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Publication date2018
Article number13
ISBN (Print)978-1-4503-6449-2
Publication statusPublished - 2018
EventEuropean Conference on Cognitive Ergonomics - Utrecht, Netherlands
Duration: 5 Sep 20187 Sep 2018
Conference number: 36


ConferenceEuropean Conference on Cognitive Ergonomics

ID: 212266084