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

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 performance during an on-going course, those at risk of graduating late than the tentative timeline. and predicts the capacity of students in a campus. The data constitutes of demographics, learning, academic, and education-related attributes that 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.