Design and Implementation of Intelligent Educational Administration System Using Fuzzy Clustering Algorithm

Sci. Program. Pub Date : 2021-12-29 DOI:10.1155/2021/9485654
Fang Liu
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引用次数: 6

Abstract

The present work aims to solve the problems that the traditional educational administration management system has, such as low efficiency in analyzing big data, and the analysis results have low value, which is based on manual rules definition in big data analysis and processing. The work proposes a student achievement prediction model FCM-CF based on Fuzzy C-means (FCM) and Collaborative Filtering (CF). The work also introduces it into the research of educational administration management to construct an intelligent educational administration management system. At the beginning, the FCM-CF model is described in detail. Then, the system requirements and specific design methods are described in detail. Eventually, with the students’ performance prediction as an example, the performance of the system is tested by designed simulation experiments. The result shows that the students’ achievement in study is closely related to their daily study performance such as preparation before class, classroom performance, attendance, extracurricular study, and homework completion. Generally, the examination scores of students are significant to their daily performances. Under the same experimental conditions, the prediction error of the FCM-CF model proposed here is less than 10.8% of that of other algorithms. The model has better prediction performance and is more suitable for the prediction of middle school students’ examination scores in educational administration management system. The innovation of intelligent educational administration management system is that, in addition to the basic information management function, it also has two other functions: students’ performance prediction analysis and teacher evaluation prediction. It can provide data support for improving teaching quality. The research purpose is to provide important technical support for more intelligent educational administration and reduce the loss of human resources in educational administration.
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基于模糊聚类算法的智能教务系统设计与实现
本工作旨在解决传统教务管理系统在大数据分析处理中基于人工规则定义,分析大数据效率低、分析结果价值低的问题。本文提出了一种基于模糊c均值(FCM)和协同过滤(CF)的学生成绩预测模型FCM-CF。并将其引入教务管理的研究中,构建一个智能化的教务管理系统。首先,对FCM-CF模型进行了详细的描述。然后,详细阐述了系统需求和具体设计方法。最后,以学生成绩预测为例,通过设计的仿真实验对系统的性能进行了测试。结果表明,学生的学习成绩与学生的课前准备、课堂表现、出勤率、课外学习、家庭作业完成等日常学习表现密切相关。一般来说,学生的考试成绩对他们的日常表现很重要。在相同的实验条件下,本文提出的FCM-CF模型的预测误差小于其他算法的10.8%。该模型具有较好的预测性能,更适合于教务管理系统中学生考试成绩的预测。智能教务管理系统的创新之处在于,除了基本的信息管理功能外,还具有学生成绩预测分析和教师评价预测两个功能。为提高教学质量提供数据支持。研究的目的是为更加智能化的教务管理提供重要的技术支持,减少教务管理中人力资源的流失。
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