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.
Salminen, J., Nagpal, M., Kwak, H., An, J., Jung, S.G., and Jansen, B. J. (2019) Confusion Prediction from Eye-Tracking Data: Experiments with Machine Learning. 9th International Conference on Information Systems and Technologies (ICIST 2019), Cairo, Egypt. 24–26 March. Article No. 5.