The Use of Information Systems to Improve Academic Supervision in Colleges

A supervisory service known as academic advice aims to familiarize the student with the university and its scientific departments, the domains in which graduates work, the facets of care, and the services the university offers to its students. The academic advising service assists students in adjusting to the university environment and taking advantage of the opportunities available to them by equipping them with fundamental knowledge and skills that raise their educational attainment. Academic advising is an important link in guiding students to achieve the best performance during the teaching and learning processes, to obtain the best educational outcomes and the best possible academic achievement. Exam anxiety, academic pressures, low achievement, a lack of study time, weak motivation to learn, low self-concept, social and economic pressures, and other issues are common during the university stage and prevent students from adjusting to the university environment. As a result, it becomes urgently necessary to have an advanced academic advising system to address all of these issues and ensure its capacity to achieve psychological harmony. By considering the factors of the student's academic level and university specialty, this study seeks to shed light on the reality of the Faculty of Management Academic Guidance Unit from the perspectives of students and faculty members. The statistical analysis results from the use of various statistical approaches demonstrate that students are generally satisfied with the many dimensions of the questionnaire on the caliber of academic extension services offered by the institution

Susceptible exposed infectious recovered-machine learning for COVID-19 prediction in Saudi Arabia

Susceptible exposed infectious recovered (SEIR) is among the epidemiological models used in forecasting the spread of disease in large populations. SEIR is a fitting model for coronavirus disease (COVID-19) spread prediction. Somehow, in its original form, SEIR could not measure the impact of lockdowns. So, in the SEIR equations system utilized in this study, a variable was included to evaluate the impact of varying levels of social distance on the transmission of COVID-19. Additionally, we applied artificial intelligence utilizing the deep neural network machine learning (ML) technique. On the initial spread data for Saudi Arabia that were available up to June 25th, 2021, this improved SEIR model was used. The study shows possible infection to around 3.1 million persons without lockdown in Saudi Arabia at the peak of spread, which lasts for about 3 months beginning from the lockdown date (March 21st). On the other hand, the Kingdom's current partial lockdown policy was estimated to cut the estimated number of infections to 0.5 million over nine months. The data shows that stricter lockdowns may successfully flatten the COVID-19 graph curve in Saudi Arabia. We successfully predicted the COVID-19 epidemic's peaks and sizes using our modified deep neural network (DNN) and SEIR model