- Log In/Sign Up
- P-ISSN2765-6934
- E-ISSN2765-7027
- KCI
ISSN : 2765-6934
Assessment of Educational Conditions for 28 National Universities in South Korea
Abstract
Purpose - In this paper, we categorize and segment the 28 national universities in South Korea and measure the degree of dissimilarity (or similarity) between pairs of ones by using dissimilarity distance matrix and cluster analysis, respectively, based on the seven quantitative evaluation of educational conditions (percentage of small-scale courses, percentage of lecture by the faculty, collection of books per student, material purchase per student, percentage of building capacity, percentage of real estate capacity and rate of accommodation) in 2015. In addition, multidimensional scaling (MDS) techniques can obtain visual representation for exploring patterns of proximities among 28 national universities based on seven attributes of educational conditions. Research design, data, and methodology - This work is carried out by the 2015 Announcement of University Information, which is provided by Ministry of Education in South Korea and utilized by multivariate analyses with CLUSTER, PROXIMITIES and ALSCAL modules in IBM SPSS 23.0. Results - We make certain that 28 national universities can be categorized into five clusters which have similar traits by applying two-stage cluster analysis. MDS is utilized to perform positioning of grouped places of cluster and 28 national universities joining every cluster. Conclusions - Both types and traits of each national university can be relatively assessed and practically utilized for each university competitiveness based on underlying results.
- keywords
- Educational Conditions, Cluster Analysis, Multidimensional Scaling.
Reference
Aldenderfer, M. S., & Blashfield, R. K. (1985). Cluster analysis. Los Angeles: Sage Publications.
Borg, I., & Groenen, P. J. F. (2005). Modern multidimensional scaling (2nd ed.). New York:Springer-Verag.
Carroll, J. D., & Chang. J. J. (1970). Generalization of the singular value (Eckart-Young) decomposition to N-way tables. Psychometrika, 35, 238-319.
Jeong, D. B. (2014). Evaluation of research performances for 28 national universities. Journal of Korean Data &Information Science Society, 25(6), 1241-1251.
Jeong, D. B. (2015). A study on cluster and positioning of domestic electronic commerce based on purchasing motivation. Journal of Korean Data & Information Science Society, 29(4), 841-856.
Park, Kyung-Ho (2010). Does the PEUL have influenced on the university’s educational competency. Journal of Educational Administration, 28(4), 63-82.
Kaufman, L., & Rousseeuw, P. (2005). Finding groups in data – An introduction to cluster analysis (2nd ed.). Hoboken, New Jersey: Hohn Wiley & Sons.
Kruskal. J. B. (1964). Major MDS based on a firm numerical analysis foundation. Psychometrika, 29, 1-27.
Kruskal, J. B., & Wish, M. (1978). Multidimensional scaling. Beverly Hills, CA.: Sage Publications.
Ministry of Education (2013). The 2015 Announcement of University Information. Sejong, Korea: Department of Academic Promotion.
Ministry of Education (2014). The 2015 Announcement of University Information. Sejong, Korea: Department of Academic Promotion.
Ministry of Education (2015). The 2015 Announcement of University Information. Sejong, Korea: Department of Academic Promotion.
Savaresi, S. M., & Boley, D. (2004). A comparative analysis on the bisecting k-means and PDDP clustering algorithm. Intelligent Data Analysis, 8, 345-362.
Shepard, R. N. (1962). Nonmetric algorithm. Psychometrika, 27, 219-246.
Takane, Y., Young. F. W., & DeLeeuw, J. (1977). Combined all previous major MDS developments into a single unified algorithm. Psychometrika, 42, 7-67.
Torgerson. W. S. (1952). Multidimensional scaling: 1. Theory and method. Psychometrika, 17, 401-419.
Yang, B. H. (2013). Understanding multivariate analysis. Seoul: Communication Books.
- Downloaded
- Viewed
- 0KCI Citations
- 0WOS Citations