Data-Driven Career Placement Examination System with Prediction Model in Forecasting Licensure Performance Using Regression Techniques

Abstract

Education plays a vital role in the development of a country, and predicting the students' performance is essential to identify future risks they might encounter and enable academic institutions to take corrective actions to prevent them from failure. This study used the descriptive and developmental method of research, and criterion sampling was used to identify/select the individuals who can provide the best information for the objective of this study. After gathering the Career Placement Exam (CPE) results, the output is now imported to the developed predictive data analysis tool on which the simple-linear regression is used. Since the CPE results are not strong enough to verify the predicted result, all the undergraduate semestral grades are also used and subdivided for each of the seven technical subjects/areas, where a multilinear regression model is used. Overall, regarding Security, Functionality, Usability, Reliability, and Portability, the level of acceptance for the developed prototype system is Highly Acceptable. Moreover, for the result of the level of accuracy using the simple linear regression model (for the CPE) and the multilinear regression model (for the seven technical areas), the accuracy level of >=85 is based on the predicted and actual data generated in the Analytics tool Using the equation/model derived from linear regression techniques, the machine learning prototype can determine whether the students can pass or fail the CAAP Licensure Examination as follows: if α≤79.99, then the student will fail; if 85.00≤α≥80.00, then it is questionable for the student to pass and if α≥85.01 then the student will likely pass the licensure examination.