Academic advising plays a crucial role in the achievement of the educational institution purposes. It is an essential element in solving students' academic problems and maximizing their satisfaction and loyalty. Universities around the world have always tried to improve academic advising to personalize the student's experience. In fact, technology has the power to improve the advising process and facilitate its corresponding tasks and this has historically taken different forms. Accordingly, this paper provides an overview of academic advising and the technologies proposed to improve it. The authors present a systematic literature review on research papers that proposed an electronic academic advising system to view the research trends and identify electronic academic advising major challenges. The main contribution of this paper is to survey the different aspects and trends about the electronic systems that have been proposed to serve academic advising. This paper is a part of major research that aims to transfer the traditional academic advising to one based on Artificial Intelligence, via the current phase of academic advising.
Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.
With the aim of achieving a global ranking and academic distinction, a large number of universities have decided to focus on competition and greater academic quality on a global scale. During the course of such a journey, universities have to face numerous challenges, including the enhancement of organizational efficiency. In the context of organizational efficiency, the most significant pillar supporting this drive is recognized as being digital transformation. It is widely accepted that digital transformation allows electronic systems to be used in the process of teaching and learning. These electronic systems (e-services) enhance universities’ operational efficiency. Keeping this in mind, this research paper aims to analyze the impact of digital transformation on the organizational and spending efficiency of universities, with a special focus on one particular e-service provided by the Saudi University. For this, the study examines the effort made by the government to spread the culture of rationalization and improve the efficiency of spending through a case study involving a statistical analysis of real data from an electronic system. The results of the study state that an increase in the number of subject withdrawals will weaken the spending and organizational efficiency of the University.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.