Predictive Model Using a Machine Learning Approach for Enhancing the Retention Rate of Students At-Risk

Abstract

Student retention is a widely recognized challenge in the educational community to assist the institutes in the formation of appropriate and effective pedagogical interventions. This study intends to predict the students at-risk of low performances during an on-going course, those at-risk of graduating late than the tentative timeline and predicting the capacity of students in a campus. The data constitutes of demographics, learning, academic and educational related attributes which are suitable to deploy various machine learning algorithms for the prediction of at-risk students. For class balancing, Synthetic Minority Over Sampling Technique, is also applied to eliminate the imbalance in the academic award-gap performances and late/timely graduates. Results reveal the effectiveness of the deployed techniques with Long short-term Memory (LSTM) outperforming other models for early prediction of at-risk students. The main contribution of this work is a machine learning approach capable of enhancing the academic decision making related to student performance.