A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space

Abstract

The prediction and analysis of students’ academic performance are essential tools for educators and learners to improve teaching and learning methods. Effective predictive methods assist learners in targeted studying based on forecast results, while effective analytical methods help educators design appropriate educational content. However, in actual educational environments, factors influencing student performance are multidimensional across both temporal and spatial dimensions. Therefore, a student performance prediction and analysis method incorporating multidimensional spatiotemporal features has been proposed in this study. Due to the complexity and nonlinearity of learning behaviors in the educational process, predicting students’ academic performance effectively is challenging. Nevertheless, machine learning algorithms possess significant advantages in handling data complexity and nonlinearity. Initially, a multidimensional spatiotemporal feature dataset was constructed by combining three categories of features: students’ basic information, performance at various stages of the semester, and educational indicators from their places of origin (considering both temporal aspects, i.e., performance at various stages of the semester, and spatial aspects, i.e., educational indicators from their places of origin). Subsequently, six machine learning models were trained using this dataset to predict student performance, and experimental results confirmed their accuracy. Furthermore, SHAP analysis was utilized to extract factors significantly impacting the experimental outcomes. Subsequently, this study conducted data ablation experiments, the results of which proved the rationality of the feature selection in this study. Finally, this study proposed a feasible solution for guiding teaching strategies by integrating spatiotemporal multi-dimensional features in the analysis of student performance prediction in actual teaching processes.