A Predictive Model for Student Academic Performance in Online Learning System

Abstract

As COVID-19 has brought a major disruption in educational system, online learning has replaced the traditional classroom learning during the pandemic in many parts of the world. One of the inferior point of online learning is that it has less interaction between the instructors and the learners. Therefore, closed and active monitoring of the student's academic activities is especially required in this mode of learning. The predictive model becomes one of the main tool for active monitoring that allows the instructors to forecast the final performance of the students to determine appropriate guidance or attention for each student. In this study, predictive models based on machine learning models for student performance are proposed using the log files that record student activities in online learning system. Different sets of features are used to determine the most suitable machine learning model. Several preprocessing methods are employed to improve the performance prediction including handling imbalanced data with Synthetic Minority Over-sampling Technique (SMOTE) and choosing relevant features by XGBoost model. The model with the highest performance yields the prediction accuracy as high as 83.95%. © 2022 IEEE.