Recent advances in computer vision have opened the door for scalable eye tracking using only a webcam. Such solutions are particularly useful for online educational technologies, in which a goal is to respond adaptively to students’ ongoing experiences.
This article uses WebGazer, a webcam-based eye tracker, to automatically detect covert cognitive states during an online reading-comprehension task related to task-unrelated thought and comprehension. Across two studies with different populations, the webcam-based eye tracker provided sufficiently accurate and precise gaze measurements to predict both task-unrelated thought and reading comprehension from a single calibration.
The authors also present initial evidence of predictive validity, including a positive correlation between predicted rates of task-unrelated thought and comprehension scores, along with slicing analyses to examine performance under different conditions and generalizability across datasets.
