Application of Discriminant Analysis to Predict Students’ Performances in Mathematics in Advanced Secondary Schools

Abstract

This quantitative study aimed to use discriminant analysis procedures, to develop a classification model to be used for prediction, to predict students’ performances in Mathematics in advanced secondary schools in Tanzania. The study was conducted in Iringa Rural District to model students’ performances in Mathematics in advanced secondary schools owned by the government. Secondary data of students’ performances in Mathematics of 126 students when they were form five in the year 2020/2021 were collected from academic students’ progressive reports and three distinct groups each contained 42 students’ performances were formed. The analysis was done by using R programming software and a seed of 66 was used during the data partitioning to create training and test datasets. The maximum posterior probability rule was used as a classification rule to assign students’ performances in Mathematics into three proposed groups which are: High, Medium and Low. The classification accuracy achieved by the classification model to classify students’ performances in the training dataset is 97.33%. During validation, the model achieved the classification accuracy of 96.08% to classify students’ performances in the test dataset. These findings imply that, the classification model is valid and reliable. Hence the model is convenient to be used for prediction, to predict students’ performances in Mathematics in Advanced Certificate of Secondary Education Examinations.