Predicting Licensure Exam Success: A Mathematical Model for Engineering Students at Nueva Vizcaya State University

This research examines the correlation between the academic achievement and licensing test outcomes of electrical engineering (EE) and mechanical engineering (ME) graduates from Nueva Vizcaya State University (NVSU) in the Philippines over a five-year span. This study used a quantitative research technique involving a descriptive-correlational approach, trend analysis, and path analysis to examine data from graduates who underwent licensing examinations for the first time during this period. The results showed a significant correlation between academic achievement in certain subject areas and success in licensing exams for graduates in electrical engineering (EE) and mechanical engineering (ME). The equation for calculating the Board Rating for EE graduates is: Board Rating = 125.430 - (17.581 * ESAS) + (12.208 * MATH) - (13.011 * EE). The logistic regression equation is P = 1/(1 + e^(-(24.99651 + (5.812567 * MATH) - (3.72252 * ESAS) - (10.1496 * EE)), while the discriminant equation is D = -13.577 - (3.943 * MATH) + (2.723 * ESAS) + (6.134 * EE). The formula for calculating the Board Rating for ME graduates is as follows: Board Rating = 121.578 - (10.387 * IPPE) - (5.980 * MATHA) - (0.721 * MACHINE). The logistic regression equation is P = 1/(1 + e^(-(16.65924 - 1.99212 * MATHA - 5.60296 * IPPE + 2.329647 * MACHINE)), while the discriminant equation is D = -11.573 + 5.823 * IPPE + 0.931 * MATHA - 2.592 * MACHINE. Path analysis clarified both the direct and indirect impacts of academic success on the licensing test results. Mathematical models provide useful insights for engineering education, highlighting the need for focused curriculum creation and student assistance in engineering education programs. This research emphasizes the importance of certain academic accomplishments as predictors of success in professional licensing exams.