The purpose of this study is to analyze the College evaluation criteria diagnosing basic competencies using big data analysis. Three types of the data were collected for this study: various documents from the Ministry of Education, Internet news articles and information from twit. Through these data...
The purpose of this study is to analyze the College evaluation criteria diagnosing basic competencies using big data analysis. Three types of the data were collected for this study: various documents from the Ministry of Education, Internet news articles and information from twit. Through these data, various stakeholders including Education authorities, local officials, students, faculty and professors were identified. In order to collect data, Web scraping known as an automated method of text big data analysis was used. Twenty nine press releases from the Ministry of Education press releases, 3,214 Internet news articles, 427 twits were collected and used as research data from August 13, 2013, until August 31, 2019. After the data was collected, only nouns were extracted using R program after data pre-processing. Also, Term frequency(TF) and Term frequency-inverse document frequency(TF-IDF) weights were performed, and the last extracted keyword was presented in word-cloud form. When comparing the results of keyword extraction of the three data, there were no common keywords, and the frequency of keywords differed depending on the characteristics of the data. In the word clouds of one and two cycles, words related to the diagnosis result appear in common, and critical references to the result are continuously emerged. Although there were no same keywords in the three data, keywords were divided into four categories: ‘reduction in student enrollment’, ‘result of diagnosis’, ‘region’, ‘criticism about the diagnosis’.
After keyword extraction, the current diagnostic indices were classified into three components, I (Input) - E (Environment)- O (Output), and evaluation indices that needed to be corrected and supplemented were presented. Indices intended to be modified and supplemented include ‘subdivision of corporate accountability index’, ‘the number of students per full-time faculty member’, and the ‘scholarship support’, ‘student enrollment index’ that reflects the uniqueness of the region.
The results showed that there was a difference between the goals of diagnosing basic competencies and the actual implementation. Also, current index for diagnosing basic competencies in college is more focused on Output than on Environment. This requires diversification of the use of evaluation results, diversification of evaluation scales, and efforts to collect opinions from various stakeholders. Such efforts will enable management of the overall quality of higher education in Korea.
This study has a methodological significance in that it uses big data analytics to identify various needs and opinions of education policy. The study is also meaningful in that it has found that there are differences in the goals and practices of the college basic competency diagnosis policy, and that the current evaluation of higher education in Korea is not suitable for improving the quality of higher education. The results of this study could be utilized to establish education quality assurance policy in Korea higher education.The purpose of this study is to analyze the College evaluation criteria diagnosing basic competencies using big data analysis. Three types of the data were collected for this study: various documents from the Ministry of Education, Internet news articles and information from twit. Through these data, various stakeholders including Education authorities, local officials, students, faculty and professors were identified. In order to collect data, Web scraping known as an automated method of text big data analysis was used. Twenty nine press releases from the Ministry of Education press releases, 3,214 Internet news articles, 427 twits were collected and used as research data from August 13, 2013, until August 31, 2019. After the data was collected, only nouns were extracted using R program after data pre-processing. Also, Term frequency(TF) and Term frequency-inverse document frequency(TF-IDF) weights were performed, and the last extracted keyword was presented in word-cloud form. When comparing the results of keyword extraction of the three data, there were no common keywords, and the frequency of keywords differed depending on the characteristics of the data. In the word clouds of one and two cycles, words related to the diagnosis result appear in common, and critical references to the result are continuously emerged. Although there were no same keywords in the three data, keywords were divided into four categories: ‘reduction in student enrollment’, ‘result of diagnosis’, ‘region’, ‘criticism about the diagnosis’.
After keyword extraction, the current diagnostic indices were classified into three components, I (Input) - E (Environment)- O (Output), and evaluation indices that needed to be corrected and supplemented were presented. Indices intended to be modified and supplemented include ‘subdivision of corporate accountability index’, ‘the number of students per full-time faculty member’, and the ‘scholarship support’, ‘student enrollment index’ that reflects the uniqueness of the region.
The results showed that there was a difference between the goals of diagnosing basic competencies and the actual implementation. Also, current index for diagnosing basic competencies in college is more focused on Output than on Environment. This requires diversification of the use of evaluation results, diversification of evaluation scales, and efforts to collect opinions from various stakeholders. Such efforts will enable management of the overall quality of higher education in Korea.
This study has a methodological significance in that it uses big data analytics to identify various needs and opinions of education policy. The study is also meaningful in that it has found that there are differences in the goals and practices of the college basic competency diagnosis policy, and that the current evaluation of higher education in Korea is not suitable for improving the quality of higher education. The results of this study could be utilized to establish education quality assurance policy in Korea higher education.
The purpose of this study is to analyze the College evaluation criteria diagnosing basic competencies using big data analysis. Three types of the data were collected for this study: various documents from the Ministry of Education, Internet news articles and information from twit. Through these data, various stakeholders including Education authorities, local officials, students, faculty and professors were identified. In order to collect data, Web scraping known as an automated method of text big data analysis was used. Twenty nine press releases from the Ministry of Education press releases, 3,214 Internet news articles, 427 twits were collected and used as research data from August 13, 2013, until August 31, 2019. After the data was collected, only nouns were extracted using R program after data pre-processing. Also, Term frequency(TF) and Term frequency-inverse document frequency(TF-IDF) weights were performed, and the last extracted keyword was presented in word-cloud form. When comparing the results of keyword extraction of the three data, there were no common keywords, and the frequency of keywords differed depending on the characteristics of the data. In the word clouds of one and two cycles, words related to the diagnosis result appear in common, and critical references to the result are continuously emerged. Although there were no same keywords in the three data, keywords were divided into four categories: ‘reduction in student enrollment’, ‘result of diagnosis’, ‘region’, ‘criticism about the diagnosis’.
After keyword extraction, the current diagnostic indices were classified into three components, I (Input) - E (Environment)- O (Output), and evaluation indices that needed to be corrected and supplemented were presented. Indices intended to be modified and supplemented include ‘subdivision of corporate accountability index’, ‘the number of students per full-time faculty member’, and the ‘scholarship support’, ‘student enrollment index’ that reflects the uniqueness of the region.
The results showed that there was a difference between the goals of diagnosing basic competencies and the actual implementation. Also, current index for diagnosing basic competencies in college is more focused on Output than on Environment. This requires diversification of the use of evaluation results, diversification of evaluation scales, and efforts to collect opinions from various stakeholders. Such efforts will enable management of the overall quality of higher education in Korea.
This study has a methodological significance in that it uses big data analytics to identify various needs and opinions of education policy. The study is also meaningful in that it has found that there are differences in the goals and practices of the college basic competency diagnosis policy, and that the current evaluation of higher education in Korea is not suitable for improving the quality of higher education. The results of this study could be utilized to establish education quality assurance policy in Korea higher education.The purpose of this study is to analyze the College evaluation criteria diagnosing basic competencies using big data analysis. Three types of the data were collected for this study: various documents from the Ministry of Education, Internet news articles and information from twit. Through these data, various stakeholders including Education authorities, local officials, students, faculty and professors were identified. In order to collect data, Web scraping known as an automated method of text big data analysis was used. Twenty nine press releases from the Ministry of Education press releases, 3,214 Internet news articles, 427 twits were collected and used as research data from August 13, 2013, until August 31, 2019. After the data was collected, only nouns were extracted using R program after data pre-processing. Also, Term frequency(TF) and Term frequency-inverse document frequency(TF-IDF) weights were performed, and the last extracted keyword was presented in word-cloud form. When comparing the results of keyword extraction of the three data, there were no common keywords, and the frequency of keywords differed depending on the characteristics of the data. In the word clouds of one and two cycles, words related to the diagnosis result appear in common, and critical references to the result are continuously emerged. Although there were no same keywords in the three data, keywords were divided into four categories: ‘reduction in student enrollment’, ‘result of diagnosis’, ‘region’, ‘criticism about the diagnosis’.
After keyword extraction, the current diagnostic indices were classified into three components, I (Input) - E (Environment)- O (Output), and evaluation indices that needed to be corrected and supplemented were presented. Indices intended to be modified and supplemented include ‘subdivision of corporate accountability index’, ‘the number of students per full-time faculty member’, and the ‘scholarship support’, ‘student enrollment index’ that reflects the uniqueness of the region.
The results showed that there was a difference between the goals of diagnosing basic competencies and the actual implementation. Also, current index for diagnosing basic competencies in college is more focused on Output than on Environment. This requires diversification of the use of evaluation results, diversification of evaluation scales, and efforts to collect opinions from various stakeholders. Such efforts will enable management of the overall quality of higher education in Korea.
This study has a methodological significance in that it uses big data analytics to identify various needs and opinions of education policy. The study is also meaningful in that it has found that there are differences in the goals and practices of the college basic competency diagnosis policy, and that the current evaluation of higher education in Korea is not suitable for improving the quality of higher education. The results of this study could be utilized to establish education quality assurance policy in Korea higher education.
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