A Regression Model to Predict Key Performance Indicators in Higher Education Enrollments

Performance Indicators (KPIs) are essential factors for the success of an organization. KPIs measure the current performance and identify the ongoing progress for specified business objectives. The Ministry of Higher Education (MoHE) in Palestine used established formulas to predict the KPI. These KPIs are vital for charting the organization aims. This study applies regression models for student enrollment data sets to predict accurate KPIs that can be used and adapted for any higher education system. The predictive engine will determine the KPI based on linear regression techniques such as Lasso, Elastic Net, and non-linear regression such as Support Vector Regression (SVR), and K-Nearest Neighbor (KNN). The Ministry of Higher Education (MoHE) in Palestine provided the datasets related to enrollments and graduations data for different Higher Education Institutions (HEIs). The regression algorithms were evaluated by mean absolute error, mean square error (MSE), root mean square error (RMSE) and the R Squared. The experiment demonstrates that the 40% training with 60% testing splitting using linear regression shows the best result.

Automated summary

Performance indicators are crucial for organizational success as they measure current performance and track progress towards business objectives. This study utilized regression models to predict accurate KPIs based on student enrollment data, demonstrating that using linear regression with a 40% training and 60% testing split produced the best results.