Student Retention Prediction in Higher Learning Institutions: The Machakos University College Case

Abstract

Student retention has become one of the most important priorities for decision makers in higher learning institutions (HLI). Improving student retention starts with a thorough understanding of the reasons behind the attrition. In this study, using student demographic and institutional data along with several business intelligence (BI) techniques, we developed prototype to predict likelihood of student persistence or dropout. This study used classification models generated using Waikato Environment for Knowledge Analysis (WEKA). The model was built using the 10-fold cross validation, and holdout method (60% of the data was used as training and the remaining as test and validation). Random sampling techniques were used in selecting the datasets. The attribute selection analysis of the models revealed that the student age on entry, parent occupation, health of student and financial variables are among the most important predictors of the phenomenon. Results of the classifiers were compared using accuracy level, confusion matrices and speed of model building benchmarks. The study shows that identifying the relevant student background factors can be incorporated to design a prototype that can serve as valuable tool in predicting student withdrawal or persistence as well as recommend the necessary intervention strategies to adopt, leading to better education efficiency.