A Predictive Analytics Model for Students Grade Prediction by Supervised Machine Learning

Research on predictive analytics has increasingly evolved due to its impact on providing valuable and intuitive feedback that could potentially assist educators in improving student success in higher education. By leveraging predictive analytics, educators could design an effective mechanism to improve the academic results to prevent students' dropout and assure student retention. Hence, this paper aims to presents a predictive analytics model using supervised machine learning methods that predicts the student's final grade (FG) based on their historical academic performance of studies. The work utilized dataset gathered from 489 students of Information and Communication Technology Department at north-western Malaysia Polytechnic over the four past academic years, from 2016 to 2019. We carried out the experiments using Decision Tree (J48), Random Forest (RF), Support Vector Machines (SVM), and Logistic Regression (LR) to study the comparison performance for both classification and regression techniques in predicting students FG. The findings from the results present that J48 was the best predictive analytics model with the highest prediction accuracy rate of 99.6% that could contribute to the early detection of students' dropout so that educators can remain the outstanding achievement in higher education.