Towards a new method of estimating the student attention based on the eye gaze

Tarik Hachad, Abdelalim Sadiq, Fadoua Ghanimi, Lamiae Hachad, Ahmed Laguidi


This paper presents a new system for automating the monitoring and estimation of student attention during the course session. The followed approach is based on the analysis of the student's gaze to predict his state of attention. A simple hardware device consisting of a camera and a pc was used in this study. Existing machine learning algorithms were used for the student gaze estimation. The principles of homography were used to ensure the transformation from an image coordinates system to a real-world coordinates system. 5 students took part in this experiment and whose gaze was detected and analyzed during 10 minutes of the class session in order to analyze their states of attention and inattention.


Attention detection; Computer vision; Convolutional neural network; Eye gaze estimation; Student attention

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