映射教育技术研究:基于文本挖掘的综述

Muhammed Murat Gümüş, Volkan Kukul, Ömer Düzenli̇
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引用次数: 0

摘要

目的:本研究旨在利用文本挖掘方法确定教育技术领域的研究趋势。设计/方法/方法:本研究采用文本挖掘的方法,对b谷歌Scholar Metrics中“教育技术”子类别排名前十的期刊自2000年以来发表的10,218篇研究进行了分析。研究结果:虽然它表明集群中的文章数量每天都在增加,但一些集群的增长速度高于其他集群。目前,教育技术领域研究最多的理论是认知负荷理论和技术接受模型。当在变量方面进行检查时,可以看到动机,成功,承诺和社会存在的变量在教育技术研究中找到了自己的位置。重点:已确定教育技术领域的研究包括七个主要类别的20个专题组的文章。可以在七个标题下收集集群:评估、语言学习、教学设计、学习环境中的技术、理论基础、计算思维和在线学习。虽然可以预见其中一些问题将再次讨论,特别是在大流行之后,但人们认为需要进行更多的研究。此外,如前所述,教育技术领域被视为一个跨学科领域。由于这个原因,我们知道有更多的主题领域与这里创建的集群相关。在相似的数据集中使用不同的方法可以揭示不同的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping the Research on Educational Technology: An Overview through Text Mining
Purpose: In this study, it is aimed to determine the trends of studies in the field of educational technologies with the text mining method. Design/Methodology/Approach: In study, 10,218 studies published since 2000 by the top ten journals in the "Educational Technology" sub-category in Google Scholar Metrics were analyzed by text mining. Findings: Although it showed that the number of articles in the clusters increased daily, the rate of increase was higher in some clusters than in others. It has been seen that the most studied theories in the field of educational technologies are Cognitive Load Theory and Technology Acceptance Model. When examined in terms of variables, it is seen that the variables of Motivation, Success, Commitment and Social Presence find their place in educational technology studies. Highlights: It has been determined that the studies in the field of Educational Technologies include articles in 20 thematic clusters in seven main categories. It is possible to collect clusters under seven headings: Assessment, Language Learning, Instructional Design, Technology in Learning Environments, Theoretical Foundations, Computational Thinking and Online Learning. While it is foreseen that some of these issues will be discussed again, especially after the pandemic, some studies are thought to be needed more. In addition, the field of educational technology is seen as an interdisciplinary field, as stated before. For this reason, it is known that more subject areas are related to the clusters created here. Different perspectives can be revealed by using different methods in similar data sets.
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