Inferring Human Knowledgeability from Eye Gaze in Mobile Learning Environments

Oya Celiktutan, Yiannis Demiris

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

2 Citations (Scopus)
119 Downloads (Pure)


What people look at during a visual task reflects an interplay between ocular motor functions and cognitive processes. In this paper, we study the links between eye gaze and cognitive states to investigate whether eye gaze reveal information about an individual’s knowledgeability. We focus on a mobile learning scenario where a user and a virtual agent play a quiz game using a hand-held mobile device. To the best of our knowledge, this is the first attempt to predict user’s knowledgeability from eye gaze using a noninvasive eye tracking method on mobile devices: we perform gaze estimation using front-facing camera of mobile devices in contrast to using specialised eye tracking devices. First, we define a set of eye movement features that are discriminative for inferring user’s knowledgeability. Next, we train a model to predict users’ knowledgeability in the course of responding to a question. We obtain a classification performance of 59.1% achieving human performance, using eye movement features only, which has implications for (1) adapting behaviours of the virtual agent to user’s needs (e.g., virtual agent can give hints); (2) personalising quiz questions to the user’s perceived knowledgeability.
Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 Workshops, Proceedings
EditorsLaura Leal-Taixé, Stefan Roth
Number of pages17
Publication statusPublished - Jan 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11134 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Analysis of eye movements
  • Assistive mobile applications
  • Human knowledgeability prediction
  • Noninvasive gaze tracking


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