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




Joni Salminen, Mridul Nagpal, Haewoon Kwak, Jisun An, Soongyo Jung, Bernard J Jansen

International Conference on Information Systems and Technologies

PublisherAssociation for Computing Machinery

2019

icist 2019: Proceedings of the 9th International Conference on Information Systems and Technologies

ACM International Conference Proceeding Series

978-1-4503-6292-4

DOIhttps://doi.org/10.1145/3361570.3361577(external)

https://research.utu.fi/converis/portal/detail/Publication/45654361(external)



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.


Last updated on 2024-26-11 at 12:15