Utilizing the eXtreme Gradient Boosting Algorithm for Artificial Intelligence-supported Learning Analytics Application

Abstract

Recent technological advancements, including internet-based distance education and artificial intelligence-supported learning analytics, have significantly impacted the field of education. These advancements not only enhance the efficiency of education but also broaden access to learning while mitigating barriers to implementation. AI-supported learning analytics emerges as a pivotal tool for interpreting data gleaned from educational processes and stakeholders, thereby enhancing educational processes and outcomes. This tool streamlines the measurement, analysis, and evaluation of learning processes, encompassing a wide array of factors and parameters. Moreover, it contributes to the development of personalized and adaptive learning environments. In this study, a predictive model utilizing the XGBoost algorithm has been developed to analyze students' academic achievements. The model forecasts final exam grades based on various student characteristics, including age, participation rate, and exam scores. Evaluating the performance of the AI model involves metrics such as Mean Squared Error, Mean Absolute Error, and R² score. In findings indicate a strong prediction performance, with an R² score of 0.819. As a result of underscore the potential of AI-supported learning analytics as an effective tool for predicting and enhancing student academic performance.

Keywords

• Artificial intelligence
• Learning analytics
• Machine learning
• XGBoost algorithm

References

1. Cosar, M. (2024). Utilizing the eXtreme gradient boosting algorithm for artificial intelligence-supported learning analytics application. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 28, 277-285.