Emotion Analysis of Art Class Students Based on Machine Learning

Abstract

Purpose The study's primary objective is to investigate the association between self-efficacy, convenience, perceived usefulness, attitude, and emotions. This paper also sought to comprehend the mediating function of attitude. Methodology / Approach This study utilized a quantitative, cross-sectional design. Using a self-administered questionnaire adapted from previous studies, the data was obtained. 70% of the responses were usable. The study was analyzed using the PLS-SEM and PLS-3.3.9 software packages. Findings The study's findings indicate that attitude has a significant influence on shaping emotions. Furthermore, convenience and perceived utility influence student attitudes toward technology use. Conversely, self-efficacy does not significantly influence students' attitudes. Practical Implications These findings are essential for academicians' future research. These findings are also helpful for the formulation of technology adoption policies for students. Originality This study is one of the very few that examine machine learning in the context of the education industry.