Exploration and Strategy Analysis of Mental Health Education for Students in Sports Majors in the Era of Artificial Intelligence - Liang, Liang; Zheng, Yong; Ge, Qiluo; Zhang, Fengrui (Frontiers in Psychology, 2022)
This exploration aims to explore new educational strategies suitable for college students' mental health education. The big data and artificial intelligence (AI) are combined to evaluate the mental health education of college students in sports majors. First, the research status of college students' mental health education is introduced. The Internet of Things (IoT) mental health education structure based on big data and convolutional neural network (CNN) is constructed. Next, the survey design and questionnaire survey are carried out. Finally, the questionnaire data are analyzed and compared with the mental health status under traditional education. The results show that CNN model has good accuracy and ability to distinguish symptoms, so it can be applied to the existing psychological work in colleges; in the symptom comparison survey, under the traditional education and big data network, the number of college students with mild mental health problems is 158 (84.9%) and 170 (91.4%), respectively. It indicates that the number of college students with moderate mental health problems decreases significantly. In the comparative investigation of the severity of mental problems, the number of students with normal mental health, sub-health and serious mental health problems under the background of traditional mental health education are 125 (67.2%), 56 (30.1%) and 5 (2.7%), respectively. The mental health status of college students under the influence of big data network mental health education is better than that of traditional mental health education. There are 140 students with normal mental health, a year-on-year increase of 16.7%. In the comparative survey of specific mental disorders, students with obsessive-compulsive symptoms under traditional mental health education account for 22.0% of the total sample, having the largest proportion. In the sub-health psychological group under the big data network mental health education, the number of hostile students decreases by 7, which is the most obvious psychological improvement factor. Hence, the proposed path of mental health education is feasible.