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首页> 外文期刊>International Journal of Electrical and Computer Engineering >A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks
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A Predictive Model for Mining Opinions of an Educational Database Using Neural Networks

机译:基于神经网络的教育数据库观点挖掘预测模型

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摘要

Assessing the performance of an educational institute is a prime concern in an educational scenario. Educational Data Mining (EDM) considers several tasks originated from an educational context. One of the tasks identified is providing feedback for supporting instructors, administrators, teachers, course authors in decision making and thereby enable them to take appropriate remedial action. In this research, we have developed a prototype Neural Network Model which is trained to predict the performance of an educational institution. A Multilayer Perceptron Neural Network (MLP) model had been developed for this proposed research. The network is trained by back propagation algorithm. Data was obtained from a well-defined questionnaire consisting of 14 questions in the domains namely Academic Schedule, International Exposure, Jobs and Internship, Quality of the college, and Life at Campus. The results of these questions have been taken as inputs and performance of the institute has been considered as the output. To, validate the results generated by the network, statistical techniques have been used for the purpose. In this proposed research performance of an educational institution has been predicted. The results generated by the Neural Network and the statistical techniques have been compared in this research and it is observed that, both the methods have generated accurate results. The results have been considered based on the Normalized System Error (NSE) values of the network. A prototype Neural Network model has been developed to assess the performance of an educational institution.
机译:在教育场景中,评估教育机构的绩效是首要考虑的问题。教育数据挖掘(EDM)考虑了一些源自教育环境的任务。确定的任务之一是为决策者提供支持教师,管理员,教师,课程作者的反馈,从而使他们能够采取适当的补救措施。在这项研究中,我们开发了原型神经网络模型,该模型经过训练可以预测教育机构的绩效。多层感知器神经网络(MLP)模型已针对此拟议的研究开发。通过反向传播算法训练网络。数据是从定义明确的调查表中获得的,该调查表包括以下领域中的14个问题:学术时间表,国际曝光,工作和实习,大学质量和校园生活。这些问题的结果已作为输入,研究所的业绩被视为输出。为了验证由网络生成的结果,已将统计技术用于此目的。在该提议的研究中,已经预测了教育机构的研究表现。本研究对神经网络和统计技术产生的结果进行了比较,发现这两种方法均产生了准确的结果。已基于网络的标准化系统错误(NSE)值考虑了结果。已开发了原型神经网络模型来评估教育机构的绩效。

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