Machine learning in geography education: Evaluating student performance in rural china

Abstract

The disparity in the development and application of educational resources between urban and rural areas is pronounced, with rural students often encountering significant challenges in acquiring geographic knowledge and developing spatial cognitive skills due to their geographical isolation and limited access to educational resources. This study aims to investigate the effectiveness of geography education resource development in rural areas by examining the influence of various educational resource characteristics on students' scores in geography education. Through the application of data analysis and machine learning predictive methods, this research explores the impact of 13 variables, including gender, age, parental education level, family background, internet accessibility, outdoor practical activities, and family outdoor travel frequency. These factors were modeled and assessed using a range of machine learning algorithms. The findings reveal differential impacts of these characteristics on students' scores in geography education, with models such as Random Forest and XGBoost demonstrating superior performance in predicting scores in geography courses. This research provides empirical data and a scientific framework to support the optimization of geography education resources in rural areas, offering theoretical and practical insights for advancing educational equity and fostering local socioeconomic development.