Platform for Evaluation of Readers’ Implicit Feedback using Eye-Tracking

M. Zivkovic, E.L. van den Broek, Frans van der Sluis

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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 languageEnglish
Title of host publicationProceedings of the 30th European Conference on Cognitive Ergonomics (ECCE'18)
EditorsEgon L. van den Broek, Herre van Oostendorp, Françoise Détienne, Christian Stary
Place of PublicationNew York, NY, USA
PublisherAssociation for Computing Machinery
Number of pages4
ISBN (Electronic)978-1-4503-6449-2
DOIs
Publication statusPublished - 5 Sept 2018

Publication series

NameICPS: ACM International Conference Proceeding Series
PublisherACM

Keywords

  • Information eXperience
  • eye-tracking
  • Java
  • WEKA
  • Human-centered computing
  • Software architectures
  • Information Retrieval

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