Mitigating Academic Institution Dropout Rates with Predictive Analytics Algorithms

Abstract

Multiple studies show student dropout rates across rich, low and middle-income countries. With the development of machine learning, various learning institutions have initiated predictive analytics algorithms to solve this issue. However, there is limited knowledge on how this strategy has been employed to optimize retention rates. The research aims to comprehensively review PAAs Strategies and demonstrate how they have been effectively implemented in optimizing retention rates with a case study focusing on North America. The study employed a quantitative research methodology and thematic study design to review the existing literature. The result shows that learning institutions used a combination of data mining classifiers such as k-Nearest Neighbor, Neural Networks, Decision Tree, and Naive Bayes to categorize student dropout rates and optimize retention rates. The PAAs approach has been utilized by various educational institutions across North America, applying multiple predicting algorithms to ensure efficiency. Keywords: Predictive analytics algorithms, mitigating dropout rates, retention rates, classifiers, Learning institutions, North America