Psychological Analysis Using Artificial Intelligence Algorithms of Online Course Learning of College Students During COVID-19

COVID-19 is the worst disease caused by the coronavirus initially located in Wuhan, China in December 2019 as declared by WHO with a high transmission rate associated with serious illness. The sensitive situation of the COVID-19 educational systems shifted to online learning environments that negatively affected students’ psychological health. The prediction accuracy in diagnosis and non-infectious disease screening is improved using artificial intelligence algorithms based on clinical data for psychological symptoms with medical history of students and families. Using statistical models implemented for collecting data relevant to effects on factors relative to students such as disturbance in learning, loss of jobs, and obligations expansion for students. The analysis considers factors such as sleeping hours, social interaction, psychological state, and academic performance of students who have adopted online learning. Clustering analysis is adopted for factors affecting students in online learning platforms and data mining for data sets of students from college databases. The survey is performed with 75 teachers and 110 students connected with different online platforms for online learning and analyzing the psychological impact of implementing artificial intelligence algorithms. The novelty of the study lies in innovatively applying AI algorithms like MLP, ANN, and other different machine learning models to evaluate the psychological dimensions of online learning during COVID-19.