Analysis of Spatiotemporal Characteristics of Student Concentration Based on Emotion Evolution

Abstract

Detecting the concentration of students in the classroom can help teachers quickly understand the participation and activity of students. However, the concentration of students has complex spatiotemporal distribution and evolution laws, which is challenging to identify and quantify. This paper proposes a novel student concentration evaluation method based on emotional evolution and virus transmission, which analyzes the spatiotemporal characteristics of concentration. The research contents are as follows: (1) A visual emotion classification method based on deep learning algorithm is developed to identify and quantify the emotion changes of each student. (2) On the basis of quantification results of emotion, the concentration index model with introducing the theory of virus transmission is established and further used to explore the spread of student concentration in spatiotemporal dimensions. (3) The Wilcoxon rank sum test (RST) is used to verify the difference of the results calculated by concentration index model in different semesters, and the reliability of the model can be reflected by the Pearson correlation coefficient between the centroid of the spatiotemporal distribution of concentration and final exam results. The experiments of 64 offline courses have been carried out in a same class for two semesters, and the results show that the concentration of student in the spatial dimension can be affected by negative and positive emotions from different regions, while in the temporal dimension, the high concentration level will decrease with increase of course time, and the generation speed of this phenomenon will be further exacerbated after coupling the spatial factors.