Mathematical Models Predicting Performance in Licensure Examination of Engineering Graduates

The success of a tertiary educational institution's graduates on professional licensing exams is one of the most reliable indicators of the educational quality that the institution provides. For many years, the Philippines has produced many engineering graduates; however, in recent years, roughly 50% successfully passed the board exams required for licensure as engineers. The challenge that many higher educational institutions face is making all their engineering programs' passing rates improve and surpass the national passing rate. This study intended to develop a mathematical model capable of forecasting the performance of BSCE and BSME graduates from a private higher educational institution in the Philippines in the licensure examination administered by the Professional Regulation Commission (PRC). The researcher implemented a descriptive correlational research design. The graduates' academic performance and enrollment in a review center were assessed through descriptive statistics, which includes means, standard deviations, frequency, and percentages. Academic performance includes grades in courses tested by PRC in BSCE and BSME licensure examinations, which include mathematics and engineering courses. It also involves grades in purposive communication and integration courses. Pearson moment correlations, and ETA, were applied in determining the correlation of variables. Moreover, the strongest predictors of the Licensure Exam Areas 1, 2, and 3 were determined using stepwise regression, and mathematical models were developed using multiple linear regression. Mean Absolute Percentage Error (MAPE) was used to assess the models' predictive power. The result of the research is intended to serve as a helpful guide and basis for the school in preparing students for the licensure examination and a credible input for the institution's curriculum enhancement.