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• W4298091706 abstract "With the development of China’s economy, education has also been greatly improved, and the enrollment scale has been continuously expanded. It has become increasingly difficult to manage student performance. The traditional manual management of student grades has shortcomings such as low efficiency, poor privacy, difficulty to find and maintain, and incomplete knowledge mining of student grade data resources, so it is impossible to fully test and utilize related technologies. Based on this background, this paper designs and develops a set of decision support systems for middle school education management combined with a sparse clustering algorithm according to the actual needs of middle school education. The article first introduces the concept of sparse clustering algorithm and the four levels of the decision support system for middle school education management, data layer, model layer, application layer, and display layer, and then introduces based on five system models, namely, database design, online analysis process module design, data mining design, model library design, function design, and finally, the system model of educational decision management support is constructed, and the model test is completed. The practice shows that the system proposed in this paper can manage the current educational information of students scientifically, extract the information from the changes in students’ achievements in time, and make certain assistance for teachers’ educational decisions. This paper designs an effective decision support system by studying the sparse clustering algorithm and introducing it into the field of middle school education management." @default.
• W4298091706 created "2022-10-01" @default.
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• W4298091706 date "2022-09-30" @default.
• W4298091706 modified "2023-09-25" @default.
• W4298091706 title "A Decision Support System Model for Middle School Education Management Based on Sparse Clustering Algorithm" @default.
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• W4298091706 doi "https://doi.org/10.1155/2022/4807395" @default.
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