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

• Main Authors: SALMINEN, Joni, NAGPAL, Mridul, KWAK, Haewoon, AN, Jisun, JUNG, Soon-gyo, JANSEN, Bernard J.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: confusion detection; eye tracking; machine learning; Artificial Intelligence and Robotics; Numerical Analysis and Scientific Computing
• Online Access: https://ink.library.smu.edu.sg/sis_research/6293; https://ink.library.smu.edu.sg/context/sis_research/article/7296/viewcontent/Confusion_Prediction_from_Eye_Tracking_Data_Experiments_with_Machine_Learning.pdf
• Institution: Singapore Management University

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.