Beneficios del uso de técnicas de minería de datos para extraer y analizar datos de twitter aplicados en la educación superior: una revisión sistemática de la literatura

IF 1.5 Q2 EDUCATION & EDUCATIONAL RESEARCH Teoria de la Educacion Pub Date : 2020-02-24 DOI:10.14201/teri.22171
A. Pérez-Suasnavas, Karina Cela, W. Hasperué
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引用次数: 2

Abstract

In recent years, there has been a growing interest by education actors to include TIC in their institutions; as well as social networks, far from being a problem and their use aimed, permit innovate traditional classes and improve communication between teachers and students This study has two objectives: (1) conduct a systematic literature review through searching papers published between January/2007 and March/2019 in data bases like as ACM, IEEE, ScienceDirect, Springer and others, to evidence researches that apply data mining techniques to extract and analyze Twitters data in higher education; and (2) to emphasize pedagogic practices that include Twitter and data mining to improve education process. From 315 papers obtained, only 65 fulfilled inclusion criteria. The main results indicate that: (1) the most used data mining techniques are predictive with classification tasks; (2) Twitter is principally used to: (a) determinate perception; (b) share information, materials and resources; (c) generate communication and participation; (d) promote abilities and (e) improve oral expression and academic performance; (3) United States has the most numbers of researches in this area; however, in Latin-American countries findings are not enough, so, there a new area to investigate in this region and (4) researches used models, methods, strategies, theories and instruments as a pedagogic practice; so that, there wasn’t an agreement about a shape to include Twitter data extracting in higher education to improve teaching and learning process.
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使用数据挖掘技术提取和分析twitter数据应用于高等教育的好处:一篇系统的文献综述
近年来,教育机构越来越有兴趣在其院校加入议会;本研究有两个目标:(1)通过检索ACM、IEEE、ScienceDirect、Springer等数据库中2007年1月至2019年3月间发表的论文,对应用数据挖掘技术提取和分析高等教育twitter数据的证据研究进行系统的文献综述;(2)强调包括Twitter和数据挖掘在内的教学实践,以改善教育过程。在获得的315篇论文中,只有65篇符合纳入标准。主要结果表明:(1)最常用的数据挖掘技术是带有分类任务的预测;(2) Twitter主要用于:(a)确定感知;(b)共享信息、材料和资源;(c)促进交流和参与;(d)提升能力;(e)改善口头表达和学习成绩;(3)美国在该领域的研究最多;然而,在拉丁美洲国家,研究结果还不够,因此,在该地区有一个新的领域需要调查;(4)研究使用模型、方法、策略、理论和工具作为教学实践;因此,在高等教育中纳入Twitter数据提取以改善教学过程方面,并没有达成一致意见。
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来源期刊
Teoria de la Educacion
Teoria de la Educacion EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
4.70
自引率
6.70%
发文量
20
审稿时长
4 weeks
期刊介绍: Teoría de la Educación. Revista Interuniversitaria was founded in 1986. It is an international academic journal on Pedagogy that publishes original research articles, in open access, from a theoretical perspective and methodology of education, aiming at providing pedagogical knowledge to researchers and professionals to improve, through a discussion substantiated criticism, descriptions, explanations, understandings and applications of educational thought and action. The journal belongs to the Publication Services of the University of Salamanca. It is a biannual journal, publishing one issue per semester. The reception of articles is permanently open, welcoming original works in Spanish, English or Portuguese, admitting exceptionally proposals in other languages.
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