Leveraging Educational Data Mining: XGBoost and Random Forest for Predicting Student Achievement

Abstract

Universities and educational institutions are accumulating and storing substantial amounts of data that include the personal and educational information of students. There is an ongoing debate regarding the most crucial factors for predicting students' academic achievement, as well as determining the most suitable algorithm to employ. Furthermore, if these results are achieved, administrators need to develop better planning strategies. Educational Data Mining (EDM) is a technique used to extract specific data types from an educational system, aiding in a comprehensive understanding of students and the system itself. EDM involves transforming raw data obtained from training systems into valuable data that can facilitate data-driven decision-making. In comparison to other fields, the development of data mining and analysis in education has been relatively slow. However, mining educational data on the web presents unique challenges due to specific characteristics of the data. Although various data types possess sequential aspects, the distribution of training data over time exhibits remarkable properties. In this research, we want to find out whether alternative machine learning models, in addition to random forest, can perform comparable or even better in predicting students' academic achievement, therefore, we propose a method that utilizes XGboost and Random Forest algorithms to identify the significant factors influencing prediction accuracy.