EMPLEO DE LA REGRESIÓN LOGÍSTICA ORDINAL PARA LA PREDICCIÓN DEL RENDIMIENTO ACADÉMICO

Abstract

Higher education teachers should have as much information as possible about their students to accomplish a personalized teaching process. This is particularly important with difficult subjects where timely intervention is required to reduce academic failure rate. Ordinal logistic regression is used for building an equation that relates student's mark in Probabilistic Models of Processes (MPP according to its acronym in Spanish), which is a subject taught in the second year of Industrial Engineering studies, with first year results. Data from 274 students belonging to two different academic years were used to achieve as the best equation, the one that relates MPP grade with the average mark of science subjects received in the first year of study. It was concluded that higher values of the average mark of science subjects are associated with better MPP performance. Probability estimations got with this model were used as a starting point to develop a method that allowed the prediction of MPP condition for those students who took this subject in the academic year 2010-2011. Prediction success rate was 80%, what is regarded a good result.