A Prediction Model for At-Risk Students in Higher Education Institutions

As our nation progresses into a new era, significant attention has been focused on the realm of education, particularly higher education institutions. This aligns with the government’s efforts to equip university graduates with the necessary skills to succeed in various sectors, thereby enhancing the nation’s development in terms of economics, business, and technology. However, the forward-looking nature of this emphasis is hindered by the presence of students who are at risk of falling behind in universities. The number of publications addressing at-risk students in higher education is growing, but there is a lack of an effective method for identifying these students at an early stage. Consequently, there is a pressing need for a new model that can accurately predict which students are at risk. This research paper investigates the relevant characteristics of at-risk students, which are then used to develop a profiling system incorporated into the model. The paper also discusses a validation criterion utilizing machine learning algorithms. The ultimate impact of this model lies in its contribution to practical applications, knowledge advancement, and society.