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 important to identify future risks they might encounter and enable academic institutions to take corrective actions to prevent them from failure. The researcher used the descriptive and developmental method of research, criterion sampling is used to identify/select the individuals that can provide the best information for the objective of this study. After gathering the Career Placement Exam results the output now is imported to the developed predictive data analysis tool on where the simple-linear regression is being used. Since the CPE results was not strong enough to verify the predicted result, all the undergraduate semestral grades were also used and subdivided for each of the 7 technical subject/areas where multilinear regression model was used. Utilizing the standard ISO 9126 for software development, the survey questionnaire results were: In terms of Functionality with 3.8 General Weighted Mean was verbally interpreted as Highly Accepted. In terms of Usability with 3.64 General Weighted Mean and verbally interpreted as Highly Accepted. In Terms of Reliability with 3.3 General Weighted Mean and verbally interpreted as Moderately Accepted. In terms of Portability with 3.76 General Weighted Mean and verbally interpreted as Highly Accepted. Overall, the level of acceptance for the developed system is Highly Accepted. 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 7 technical areas) the accuracy level of >=85 based on the developed predictive model 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, then, if 85.00≤α≥80.00 then is it questionable for the student to pass and if α≥85.01 then the student will likely pass the licensure examination.