Understanding social discourse and current status of liberal arts subjects in colleges through text mining-based data analysis

Seung-Yeon Choi
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Abstract

Objectives The purposes of this study, we seek to gain a macroscopic understanding of the social discourse and current status of liberal arts subjects in colleges in order to improve the quality and strengthen the liberal arts education in colleges. Methods To this end, ‘Big data’ was collected using the keywords of ‘junior college+liberal arts’ subjects appearing on the portal site, and data analysis was conducted using the liberal arts subject schedule of junior colleges in the Chungcheong and Gangwon regions as ‘retained data’. Keyword analysis, TF-IDF weight analysis, and network centrality analysis were performed in the big data analysis platform(TEXTOM), and CONCOR analysis was performed using the UCINET6.0 program. Results First, as a result of keyword analysis, keywords such as credits, graduation, and completion appeared in portal-collected big data, and in the case of retained data, keywords such as life, English, ability, and understanding were mainly found. Second, keywords with high TF-IDF values a ppeared in the order of ‘credits’, ‘bachelor’s degree’, ‘degree’, and ‘graduation’ in the case of portal-collected big data, and there appeared to be no difference in the case of retained data. Third, as a result of keyword centrality analysis, it was found that in the case of portal big data, ‘credit’ was out of the ranking in closeness centrality, and in the case of retained data, ‘English’ was found to be out of the ranking. Fourth, the clusters formed through CONCOR analysis were found to form two clusters for portal big data and four clusters for retained data. Conclusions Big data on liberal arts subjects at colleges showed that social discourse is being formed at a very formal level, such as basic materials related to bachelor's degrees, and the data shows that the nature of liberal arts subjects based on vocational fundamentals is still strong in the composition of subjects.
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通过基于文本挖掘的数据分析了解高校文科学科的社会话语和现状
目的 本研究旨在宏观了解高校文科科目的社会话语和现状,以提高高校文科教育的质量,加强高校文科教育。为此,我们利用门户网站上出现的 "大专+文科 "科目关键词收集 "大数据",并以忠清和江原地区大专文科科目表作为 "保留数据 "进行数据分析。在大数据分析平台(TEXTOM)上进行了关键词分析、TF-IDF 权重分析和网络中心性分析,并使用 UCINET6.0 程序进行了 CONCOR 分析。结果 首先,通过关键词分析,门户采集的大数据中出现了学分、毕业、结业等关键词,而在留存数据中,主要出现了生活、英语、能力、理解等关键词。其次,在门户收集的大数据中,TF-IDF 值较高的关键词依次出现在 "学分"、"学士学位"、"学位 "和 "毕业 "中,在保留数据中似乎没有差异。第三,通过关键词中心度分析发现,在门户大数据中,"学分 "的亲近中心度排名靠后,而在保留数据中,"英语 "的亲近中心度排名靠后。第四,通过 CONCOR 分析形成的聚类发现,门户大数据形成 2 个聚类,保留数据形成 4 个聚类。结论 高校文科类学科大数据显示,社会话语正在非常正式的层面上形成,如与本科相关的基础材料,数据显示,基于职业基础的文科类学科性质在学科构成中仍然很强。
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