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A big data and neural networks driven approach to design students management system

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Abstract

Designing a Student Management System based on big data and deep learning is paramount in the modern educational landscape. This innovative approach allows institutions to harness the power of vast datasets to gain actionable insights into student performance, preferences, and learning patterns. The research begins by identifying various facets and modules within the student ecosystem that impact student success, including examination results, health records, grades analysis, demographics, co-curricular activities, teacher information, and parental details. The data pertaining to these modules are inherently distributed, and the system organizes it into a unified format using the big data tool Apache Hadoop. The goal is to consolidate the data for utilization by deep learning models in predicting student success. Apache Hadoop, as a robust big data tool, facilitates efficient storage and analysis of large datasets. Subsequently, a Feed Forward Neural Network (FNN) model is developed to extract distinctive patterns indicative of student success. The planned FNN architecture incorporates 128 neurons and employs Rectified Linear Unit (RELU) and SoftMax activation functions to enhance predictive capabilities. The increased number of neurons in the model allows for a comprehensive exploration of all student data sources, thereby improving the true positive rate. Ultimately, the computation of execution and timely interventions by teachers, parents, and administration demonstrates heightened precision resulting from the analysis and rotation creation methods, leading to an efficient student management system. A thorough comparison with earlier approaches underscores its superior effectiveness, with a noteworthy 45% increase in accuracy and a significant 55% enhancement in precision.

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Data availability

The data that support the findings of this study are available from the corresponding author, upon reasonable request.

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Funding

This study was funded by 2023 University Philosophy and Social Sciences Research Project: Research on the Training Model of Innovative Talents in Art and Design in Higher Vocational Education under the ChatGPT Rush (Grant No. 2023SJYB2117).

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Authors and Affiliations

  1. School of Arts and Crafts, Jiangsu Vocational College of Tourism, Yangzhou, 225000, Jiangsu, China

    Jingjing Fan

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  1. Jingjing Fan

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Correspondence toJingjing Fan.

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