A Machine-Learning based Approach to Support Academic Decision-Making at Higher Educational Institutions

Taking appropriate decisions in the academic processes at a university has a great impact on improving the quality of education and can have an important benefit for students, faculty members, and the entire academic community. In this paper, we propose a decision support solution providing accurate analysis, better decision support, and reporting and planning capability to assist decision-makers in order to enhance the quality of educational processes. To achieve this goal, a set of machine learning is used. Experiments are conducted on real data describing the College of Computer Science and Engineering (CCSE) at Taibah University in Saudi Arabia. Results show that we can predict graduation rates in a real case study to support decision-making. In addition, a comparison between four techniques of machine learning namely Support Vector Machine, Naïve Bayes, Decision Tree, and Random Forest is held using accuracy, recall, precision, and F-measure.