Learning attention characterization based on head pose sight estimation

Abstract

The degree of students’ attentiveness in the classroom is known as learning attention and is the main indicator used to portray students’ learning status in the classroom. Studying smart classroom time-series image data and analyzing students’ attention to learning are important tools for improving student learning effects. To this end, this paper proposes a learning attention analysis algorithm based on the head pose sight estimation.The algorithm first employs multi-scale hourglass attention to enable the head pose estimation model to capture more spatial pose features.It is also proposed that the multi-classification multi-regression losses guide the model to learn different granularity of pose features, making the model more sensitive to subtle inter-class distinction of the data;Second, a sight estimation algorithm on 3D space is innovatively adopted to compute the coordinates of the student’s sight landing point through the head pose; Finally, a model of sight analysis over the duration of a knowledge point is constructed to characterize students’ attention to learning. Experiments show that the algorithm in this paper can effectively reduce the head pose estimation error, accurately characterize students’ learning attention, and provide strong technical support for the analysis of students’ learning effect. The algorithm demonstrates its potential application value and can be deployed in smart classrooms in schools.