A Deep Learning-Based TE Method for MSs’ Mental Health Analysis

Abstract

Teaching evaluation (TE) is of great significance in education and can judge the value and appropriateness of the curriculum, which is a distinguished part of the educational work. Compared with other courses focusing on imparting knowledge, mental health not only imparts psychological knowledge but also cultivates Marxism students’ (MSs) ability to adjust psychology and maintain mental health. Therefore, the evaluation of this course has a special character. As the unity of scientific world outlook and values, Marxism can promote students’ mental health. When assessing students’ ability to maintain mental health, the influence of Marxism should be taken into account. In this study, we first established an evaluation index system in line with the actual mental health considering the influence of Marxism and put forward a deep memory network with prior information (PI-DMN) to realize the aspect-based sentiment analysis (ABSA) of the student evaluation text. Combined with students’ scoring of the course, the sentiment analysis results are used as the input dataset of LSTM model to realize the TE of mental health course. The data analysis exposes that Marxism can promote mental health, and the empirical analysis reveals that the accuracy of TE can be improved by considering the sentiment analysis of comment texts and can also be improved to a certain extent after aspect labeling of the dataset.