A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space
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...
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MDPI AG
2024-11-01
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| Series: | Mathematics |
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| author | Zheng Luo Jiahao Mai Caihong Feng Deyao Kong Jingyu Liu Yunhong Ding Bo Qi Zhanbo Zhu |
| author_facet | Zheng Luo Jiahao Mai Caihong Feng Deyao Kong Jingyu Liu Yunhong Ding Bo Qi Zhanbo Zhu |
| author_sort | Zheng Luo |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-445e7949fa9c4992bf4a8508a5fef1f0 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-445e7949fa9c4992bf4a8508a5fef1f02024-11-26T18:11:58ZengMDPI AGMathematics2227-73902024-11-011222359710.3390/math12223597A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and SpaceZheng Luo0Jiahao Mai1Caihong Feng2Deyao Kong3Jingyu Liu4Yunhong Ding5Bo Qi6Zhanbo Zhu7The School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaThe School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaThe School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaThe School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaThe School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaThe School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, ChinaSchool of Astronautics, Harbin Institute of Technology, Harbin 150001, ChinaNo. 703 Research Institute, China State Shipbuilding Corporation Limited, Harbin 150025, ChinaThe 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.https://www.mdpi.com/2227-7390/12/22/3597student performance predictiondata fusionfeature importance analysismachine learning |
| spellingShingle | Zheng Luo Jiahao Mai Caihong Feng Deyao Kong Jingyu Liu Yunhong Ding Bo Qi Zhanbo Zhu A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space Mathematics student performance prediction data fusion feature importance analysis machine learning |
| title | A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space |
| title_full | A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space |
| title_fullStr | A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space |
| title_full_unstemmed | A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space |
| title_short | A Method for Prediction and Analysis of Student Performance That Combines Multi-Dimensional Features of Time and Space |
| title_sort | method for prediction and analysis of student performance that combines multi dimensional features of time and space |
| topic | student performance prediction data fusion feature importance analysis machine learning |
| url | https://www.mdpi.com/2227-7390/12/22/3597 |
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