Predictive Modeling for Criminology Licensure Examination Success Through Mathematical Modelling

Abstract

Education is a cornerstone of societal progress, and the success of academic programs hinges on the performance of its students. In the field of Criminology, where analytical thinking, critical reasoning, and a deep understanding of societal dynamics are paramount, predicting and improving student performance holds particular significance. This research paper aims to developed a predictive model for forecasting the performance of Criminology students in their licensure examination. As such, the result of this study reveals that the forecasted criminology licensure examination (CLE) ratings for next two examination dates show a large difference in with an anticipated low passing rate on the first date, rising significantly on the second date. Thus, with this predictive rating performance of Criminology students in their licensure examination, this result now offers a valuable tool for the institution and policymakers to implement targeted interventions, ultimately enhancing the overall academic outcomes of Criminology students.