INTELLIGENT EDUCATIONAL ANALYTICS: LEVERAGING MACHINE LEARNING FOR COMPREHENSIVE ACADEMIC PERFORMANCE PREDICTION
TL;DR
The research showed that using multiple data types significantly increases the predictive power of the model, and among all methods evaluated, multiple linear regression had the best balance between accuracy and computational requirement for predicting student performance.
Abstract
The current research provides a thorough exploration into different methods of machine learning used to predict educational performance using a variety of data sources. This research studies methods for predicting academic performance and displays the difference in performance of each model, including performance measures, demographics, and behavior survey data collected from middle school-aged children. In this research, 5-fold cross-validation was useful for demonstrating increases in the accuracy of predictions with the use of multiple types of data without significantly affecting the level of computing needed for making predictions. The research showed that using multiple data types significantly increases the predictive power of the model. Additionally, among all methods evaluated, multiple linear regression had the best balance between accuracy and computational requirement for predicting student performance. The factors determined to be the most predictive of student academic performance included student behavior, the educational level of parents, socio-economic status, and historically earned grades. The study of educational data mining presented in this paper provides a valuable understanding of how different data integration configurations affect how well algorithms compare in performance. The results may be used as criteria for schools seeking to employ data-driven strategies for improving student achievement and academic performance.