Analysis on Big Data by Performance Factors of Creative Education using Semi-structured Data-based Twitter

K. Joo, Ji-Hoon Seo, N. Park
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Abstract

As various forms of big data, which includes but not limited to, large volume texts, voice data and videos, are being accumulated whilst the waves of the information age are accelerating progressively, the number of inter-disciplinary analysis solutions with capabilities to use such information is increasing, and accordingly, the developments, such as the drop of costs required for data storage and various Social Network Services, have brought forth the quantitate and qualitative stretch of the data. The phenomenon makes it possible to achieve the types of data usage which were not available in the past, and thus the potential values and leverage of data are on the rise. Studies that that apply such inter-disciplinary analysis system for the improvement of the educational system to suggest future-oriented education system are being carried out at progressive levels. This study has carried out an analysis on big data with Twitter as its subject and suggested, via the natural language process of data and frequency analysis, the quantitative scale indicative of how various issues and performances relating to creative education in South Korea have been handled.
基于半结构化数据Twitter的创意教育绩效因素大数据分析
随着各种形式的大数据(包括但不限于大量文本、语音数据和视频)的积累,以及信息时代浪潮的不断加速,能够利用这些信息的跨学科分析解决方案越来越多,因此,数据存储和各种社交网络服务所需成本的下降等发展,提出了数据的定量和定性的延伸。这种现象使得实现过去无法获得的数据使用类型成为可能,因此数据的潜在价值和杠杆作用正在上升。将这种跨学科分析系统应用于改善教育制度以建议面向未来的教育制度的研究正在逐步进行。本研究以Twitter为研究对象,对大数据进行了分析,并通过数据的自然语言过程和频率分析,提出了反映韩国创意教育中各种问题和表现处理情况的量化尺度。
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