Confusion prediction from eye-tracking data: Experiments with machine learning

Joni Salminen, Haewoon Kwak, Soon Gyo Jung, Mridul Nagpal, Jisun An, Bernard J. Jansen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Predicting user confusion can help improve information presentation on websites, mobile apps, and virtual reality interfaces. One promising information source for such prediction is eye-tracking data about gaze movements on the screen. Coupled with think-aloud records, we explore if user's confusion is correlated with primarily fixation-level features. We find that random forest achieves an accuracy of more than 70% when prediction user confusion using only fixation features. In addition, adding user-level features (age and gender) improves the accuracy to more than 90%. We also find that balancing the classes before training improves performance. We test two balancing algorithms, Synthetic Minority Over Sampling Technique (SMOTE) and Adaptive Synthetic Sampling (ADASYN) finding that SMOTE provides a higher performance increase. Overall, this research contains implications for researchers interested in inferring users' cognitive states from eye-tracking data.

Original languageEnglish (US)
Title of host publicationProceedings of the 9th International Conference on Information Systems and Technologies, ICIST 2019
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450362924
DOIs
StatePublished - Mar 24 2019
Event9th International Conference on Information Systems and Technologies, ICIST 2019 - Cairo, Egypt
Duration: Mar 24 2019Mar 26 2019

Publication series

NameACM International Conference Proceeding Series

Conference

Conference9th International Conference on Information Systems and Technologies, ICIST 2019
CountryEgypt
CityCairo
Period3/24/193/26/19

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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