@inproceedings{c11836ebdf0b4e3b99a850be16b16537,
title = "Confusion prediction from eye-tracking data: Experiments with machine learning",
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.",
keywords = "Confusion detection, Eye tracking, Machine learning",
author = "Joni Salminen and Haewoon Kwak and Jung, {Soon Gyo} and Mridul Nagpal and Jisun An and Jansen, {Bernard J.}",
note = "Publisher Copyright: {\textcopyright} 2019 Copyright is held by the owner/author(s). Publication rights licensed to ACM.; 9th International Conference on Information Systems and Technologies, ICIST 2019 ; Conference date: 24-03-2019 Through 26-03-2019",
year = "2019",
month = mar,
day = "24",
doi = "10.1145/3361570.3361577",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
booktitle = "Proceedings of the 9th International Conference on Information Systems and Technologies, ICIST 2019",
address = "United States",
}