Prediction Analysis of College Teachers’ Happiness Based on the Graph Convolutional Network

Abstract

College teachers are the source of creativity in the education system, and the happiness of college teachers can be fully ensured in order to cultivate better talents. To address the current problem that the trend of happiness of college teachers is unclear and the direction of enhancing happiness is unknown. We propose a method to predict the happiness of university teachers based on a graph convolution framework. We conduct a comprehensive analysis of teachers’ happiness factors in terms of academic innovation, job satisfaction, and student training rate to achieve a trend prediction of teachers’ happiness and provide a clear direction for improving happiness. We use the graph convolution structure to input the happiness factors as graph nodes into the graph convolution layer. We attach a sparse layer to the temporal convolution structure to obtain the time-scale information of happiness factor nodes in different research cycles. Finally, we cooperate with an e-commerce platform and use mobile app data as the feedback experimental database. The experimental results show that teachers with different years of experience perceive different levels of happiness factors, and different emotional stimuli also affect teachers' perceptions of happiness.