Predicting B.L.E.P.T. Performance of Unit Earners using Supervised Classification Algorithms

Data Mining is the extraction of knowledge using solid data available in workplaces. This is also applied in the educational system to predict the academic performance of students. In this paper a prediction of unit earners performance in the Board Licensure Examination for Professional Teachers is conducted to know the chances of noneducation graduates who wanted to pursue teaching. The researchers find out the performance of unit earners who passed the BLEPT for the past five years (2012-2016) with a total of 10 examination batches but not to include re-takers. The predictors included are the general weighted average in their undergraduate program and all grades earned in their professional courses. The data mining algorithms used came from the supervised classification algorithm category and the researcher included at least 3 classification algorithms to work on. The algorithm which has the probability accuracy will be recommended in the study.