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

Zheng Luo, Jiahao Mai, Caihong Feng, Deyao Kong, Jingyu Liu (), Yunhong Ding (), Bo Qi and Zhanbo Zhu
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Zheng Luo: The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Jiahao Mai: The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Caihong Feng: The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Deyao Kong: The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Jingyu Liu: The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Yunhong Ding: The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
Bo Qi: School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
Zhanbo Zhu: No. 703 Research Institute, China State Shipbuilding Corporation Limited, Harbin 150025, China

Mathematics, 2024, vol. 12, issue 22, 1-26

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.

Keywords: student performance prediction; data fusion; feature importance analysis; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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