电子学习中大数据的设计与编程技术

R. Khrabatyn, Viktoriia Bandura, N. Shkolna, Yuri Khrabatyn
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引用次数: 0

摘要

近年来,世界范围内的电子教育发展迅速,但主要的问题是如何及时地为学生提供优质的教育信息。这方面的一个重要推动力是2019冠状病毒病的全球流行。如果不分析教育过程中的参与者——学生、教师、管理人员等——进入电子教育信息环境的大量信息流,就无法解决实施电子教育的问题。在这种环境下,存在着大量不同类型的数据,既有结构化的,也有非结构化的,这些数据很难用传统的统计方法进行处理。该研究的目的是表明,开发和实施成功的电子学习系统需要使用新技术,这些新技术将允许存储和处理大数据流。存储大数据需要大量的磁盘空间。研究表明,利用集群技术NAS (Network Area Storage)将教育机构的信息存储在NAS服务器上,并可通过Internet访问,是解决这一问题的有效方法。为了在电子学习环境中处理和个性化大数据,建议使用MapReduce、Hadoop、NoSQL等技术。本文提供了在云环境中使用这些技术的示例。电子学习中的这些技术使教育信息的灵活性、可扩展性、可访问性、安全性、保密性和易用性成为可能。电子学习的另一个重要问题是在大数据和新知识(数据挖掘)中发现新的,有时是隐藏的关系,这些关系可用于改进教育过程并提高其管理效率。为对电子教育资源进行分类,识别具有相似心理、行为和智力特征的学生的模式(模式),提出利用大数据分析的方法开发个性化课程。文章表明,到目前为止,已经为大数据挖掘开发了许多软件应用程序。这些软件产品可用于教育信息的分类、聚类、回归和网络分析。这些方法在电子教育中的应用将使教师能够及时收到关于学生的信息,对学习过程中的任何变化做出快速反应,及时更改教育内容。所获得的研究结果将用于制定在乌克兰高等和中等教育机构开设电子课程的建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Technologies for designing and programming big data in e-learning
Recently, e-education around the world is developing rapidly and the main problem is the timely provision of students with quality educational information. A significant impetus for this is the global epidemic of covid-19. The problem of implementing e-education cannot be solved without analysing the large flow of information coming into the information environment of e-education from participants in the educational process – students, teachers, administration, etc. In this environment, there are a large number of different types of data, both structured and unstructured, which are difficult to process by traditional statistical methods. The aim of the study is to show that the development and implementation of successful e-learning systems requires the use of new technologies that would allow the storage and processing of large data streams. Large amounts of disk space are required to store large data. It is shown that to solve this problem it is expedient to use cluster technology NAS (Network Area Storage), which allows to store information of educational institutions on NAS - servers and to have access to them from the Internet. To process and personalize Big Data in the e-learning environment, it is proposed to use technologies MapReduce, Hadoop, NoSQL and others. The article provides examples of the use of these technologies in the cloud environment. These technologies in e-learning make it possible to achieve flexibility, scalability, accessibility, security, confidentiality and ease of use of educational information. Another important problem of e-learning is the discovery of new, sometimes hidden, relationships in big data, new knowledge (data mining), which can be used to improve the educational process and increase the efficiency of its management. To classify electronic educational resources, identify patterns (patterns) of students with similar psychological, behavioural and intellectual characteristics, the development of individualized curricula in the article it is proposed to use methods of big data analysis. The article shows that to date, many software applications have been developed for big data mining. These software products can be used for classification, clustering, regression and network analysis of educational information. The application of these methods in e-education will allow teachers to receive timely information about students, to respond quickly to any changes in the learning process, to make timely changes to educational content. The obtained results of the research are offered to be used for development of recommendations at creation of electronic courses in higher and secondary educational institutions of Ukraine.
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