Predicting Student Enrollment at the Education College of Benghazi via Mathematical Modeling Approaches

This study aims to demonstrate the process of three different models for predicting the number of undergraduate students enrolled at the Education College of Benghazi in a certain period. These models are Exponential change method, logistic method and the least squares method. Data was collected from registration and admissions office of the education college of Benghazi for the years 2015-2023. This data will be used to forecast for all methods that will be discussed in this research. For evaluating and comparing the efficiency and accuracy of these methods, Mean Absolute Deviation (MAD), Mean Absolute Percent Error (MAPE), and Root Mean Square Error (RMSE) were used. Enrollment data analysis allowed for the comparison of the models to the actual enrollment in regards to which method gave the best prediction of future student enrollment. The least squares method provided the most accurate estimations, with the precision rate of 92%. The three models were further used to predict enrollment levels for the Education College of Benghazi in the future semesters.