Enrolment Management in Higher Learning Institutions: Student Retention Prediction

Abstract

Student retention has become one of the most important priorities for decision makers in higher learning institutions (HLI). The increasing competition for students among tertiary institutions has resulted in a greater emphasis on student retention. Improving student retention starts with a thorough understanding of the reasons behind the attrition. In an effort to address this issue, the study used student demographic and institutional data along with several business intelligence (BI) techniques and analytical tools, to develop prototype to predict likelihood of student persistence or dropout with the goal to identify factors that can be used to identify students who are at risk of dropping out of tertiary institution program. 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 business intelligence system 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 and graduation rate.