A Plain Bayesian Algorithm-Based Method for Predicting the Mental Health Status and Biomedical Diagnosis of University Students

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 5
• Main Author: Wang, Jiao
• Format: Journal Article
• Language: English
• Published: United States Hindawi 28.08.2022; John Wiley & Sons, Inc
• Subjects: Adaptability; Algorithms; Bayes Theorem; Bayesian analysis; California; Case studies; College students; Colleges & universities; Coronaviruses; Diagnosis; Distance learning; Education; Epidemics; Health Status; Humans; Independent study; Internet; Learning; Mental health; Online education; Online instruction; Psychological aspects; Quality assessment; Reproducibility of Results; Saudi Arabia; Students; Universities; University students; Saudi Arabia; California
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/2617488

The purpose of this study was to assess e-learning during Corona epidemic regarding advantages, limitations, and their recommendations for managing learning during the epidemic. Based on a case study, this study used qualitative research. Sixteen students from King Saud University’s College of Education were invited to take part. These students receive their online lectures via the “Zoom” application. A 20-minute WhatsApp one-on-one semiorganized interview was likewise utilized. To guarantee the reliability, iCloud was utilized to record gatherings and meetings for direct record (adaptability, constancy, confirmability, and validity). Results were presented in three themes: advantages of employing distance education, limitations of usages, and recommendations for improvements. Analyzing the feedbacks collected from students by the four interviewers, important characteristics of distance education emerged. They were student-centered learning, which included: comfortable, self-directed learning, asynchronous learning, and flexibility. The most common limitations associated with distance education, in general, included inefficiency, that is, lack of student feedback, and lack of attentiveness. As for recommendations for improvements the most obvious characteristics that became evident in students’ responses were teaching and assessment and quality enhancement.