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http://dx.doi.org/10.9708/jksci.2022.27.07.115

Analysis of Keywords and Language Networks of Pedagogical Problems in the Secondary-School Teacher's Employment Exam : Focusing on the 2019~2022 School Year Exam  

Kwon, Choong-Hoon (Dept. of Secondary Special Education, Kwangju Women's University)
Publication Information
Journal of the Korea Society of Computer and Information / v.27, no.7, 2022 , pp. 115-124 More about this Journal
Abstract
The purpose of this study is to analyze and present keywords, trends, and language networks of keywords for each year of the pedagogical exam of the secondary teacher's employment exam for the 2019~2022 school year. The main research methods were text mining technique and language network analysis method, and analysis programs were KrKwic, Wordcloud Maker, Ucinet6, NetDraw, etc. The research results are as follows; First, keywords such as teacher, student, curriculum, class, and evaluation appeared in the top rankings, and keywords (online, wiki, discussion ceremony, information, etc.) that reflect the recent online class progress in the current COVID-19 situation also tended to appear. The keywords with high frequency of occurrence in the four-year integrated text were student(44), teacher(39), class(27), school(18), curriculum(16), online(10), and discussion method(8). Second, the overall language network of the keywords with high frequency of 4 years showed a significant level of density(0.566), total number of links(492), and average degree of links(16.4). The degree centrality was found in the order of teacher(199.0), class(197.0), student(185.0), and school(150.0). Betweenness centrality was found in the order of teacher(30.859), class(18.956), student(16.054), and school (15.745). It is expected that the results of this study will serve as data to be considered for preparatory teachers, institutions and related persons, and teachers and administrators of secondary school teacher training institutions.
Keywords
Secondary-School Teacher's Employment Exam; Pedagogical Problems; Text Mining; Keywords Analysis; Language Networks Analysis;
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Times Cited By KSCI : 3  (Citation Analysis)
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