Predicting When Teachers Look at Their Students in 1-on-1 Tutoring Sessions

We propose and evaluate a neural network archi- tecture for predicting when human teachers shift their eye-gaze to look at their students during 1-on-1 math tutoring sessions. Such models may be useful when developing affect-sensitive intelligent tutoring systems (ITS) because they can function as an attention model that informs the ITS when the student's face, body posture, and other visual cues are most important to observe. Our approach combines both feed-forward (FF) and recurrent (LSTM) components for predicting gaze shifts based on the history of tutoring actions (e.g., request assistance from the teacher, pose a new problem to the student, give a hint, etc.), as well as the teacher's prior gaze events. Despite the challenging nature of the task - we are asking the network to predict whether or not the teacher will shift her/his eye gaze during the next one- second time interval - the network achieves an AUC (averaged over 2 teachers) of 0.75. In addition, we identify some of the factors that the human teachers in our study used when making gaze decisions and show evidence that the two teachers' gaze patterns share common characteristics.