使用大数据分析的教育系统中基于认知Web服务的学习分析

Li Bin
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摘要

在教育领域,数字化学习扮演着重要的角色。随着时间的推移,数字化学习正在取代传统的教育方法。对学生素质的准确分析可以提高他们的学习成绩。随着科技和大数据的进步,大数据分析有很多应用,包括教育。大量的学术信息正在产生,而发现一种有效地利用和分析这些信息的技术对许多教育组织来说是一个具有挑战性的问题。本文提出了教育集群大数据挖掘系统(ECBDMS)。集成了基于认知web服务的学习分析(CWS-LA)系统,以对数据进行安全分类并提供对数据的方便访问。与其他现有方法相比,ECBDMS的性能提高了92.8%,预测率提高了88.6%,聚类错误率提高了2.3%,学习率提高了94%,预测准确率提高了97.1%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cognitive Web Service-Based Learning Analytics in Education Systems Using Big Data Analytics
In the field of education, digital learning plays an important part. For each passing day, digital learning is displacing the traditional method of education. An accurate analysis of a student's qualities improves their academic performance. With the advancement of technology and big data, there are many applications for big data analytics, including education. Huge volumes of academic information are being generated, and discovering a technique to harness and analyze this information effectively is a challenging issue among many educational organizations. In this paper, educational clustering big data mining system (ECBDMS) has been proposed. The cognitive web service based learning analytic(CWS-LA) system is integrated to securely categorize and provide ease of access to the data. ECBDMS has been found to improve performance gains of 92.8%, prediction ratios of 88.6%, clustering error ratios of 2.3 percent, learning percentages of 94%, and forecasting accuracy of 97.1 percent when compared to other existing methods.
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