Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
3-2019
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
Discipline
Artificial Intelligence and Robotics | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering; Intelligent Systems and Optimization
Publication
Proceedings of the 9th International Conference on Information Systems and Technologies, Cairo Egypt, 2019 March 24-26
ISBN
9781450362924
Identifier
10.1145/3361570.3361577
Publisher
Association for Computing Machinery
City or Country
Cairo, Egypt
Citation
1
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Included in
Artificial Intelligence and Robotics Commons, Numerical Analysis and Scientific Computing Commons
