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http://dx.doi.org/10.14702/JPEE.2022.565

A Study on Clustering of Core Competencies to Deploy in and Develop Courseworks for New Digital Technology  

Ji-Woon Lee (Department of Future Technology, Korea University of Technology and Education)
Ho Lee (Department of Future Technology, Korea University of Technology and Education)
Joung-Huem Kwon (Department of Future Technology, Korea University of Technology and Education)
Publication Information
Journal of Practical Engineering Education / v.14, no.3, 2022 , pp. 565-572 More about this Journal
Abstract
Card sorting is a useful data collection method for understanding users' perceptions of relationships between items. In general, card sorting is an intuitive and cost-effective technique that is very useful for user research and evaluation. In this study, the core competencies of each field were used as competency cards used in the next stage of card sorting for course development, and the clustering results were derived by applying the K-means algorithm to cluster the results. As a result of card sorting, competency clustering for core competencies for each occupation in each field was verified based on Participant-Centric Analysis (PCA). For the number of core competency cards for each occupation, the number of participants who agreed appropriately for clustering and the degree of card similarity were derived compared to the number of sorting participants.
Keywords
Card Sorting; Participant-Centric Analysis; Core Competencies; New Digital; Course Development;
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  • Reference
1 A. Donner and J. J. Koval, "The estimation of intraclass correlation in the analysis of family data," Biometrics, vol. 36, pp. 19-25, 1980   DOI
2 P. E. Shrout and J. L. Fleiss, "Intraclass Correlations : Uses in Assessing Rater Reliability," Psychological Bulletin, vol. 86, no. 2, pp. 420-428, 1979.   DOI
3 D. Spencer, Card Sorting: Designing Usable Categories, Brooklyn, NY, USA: Rosenfeld Media, 2009.
4 D. Spencer and T. Warfel, Card sorting: A definitive guide. Boxes and Arrows. Retrieved from http://boxesandarrows.com/card-sorting-a-definitive-guide/, April 2004.
5 L. A. Rojas and J. A. Macias, "Toward collisions produced in requirements rankings: A qualitative approach and experimental study," Journal of Systems and Software, vol. 158, pp. 110417, 2019.
6 B. Albert and T. Tullis, Measuring the User Experience: Collecting, Analyzing, and Presenting Usability Metrics, Morgan Kaufmann, 2013.
7 S. Paea and R. Baird, "Information Architecture (IA): Using multidimensional scaling (MDS) and k-means clustering algorithm for analysis of card sorting data," Journal of Usability Studies, vol. 13, no. 3, pp. 138-157, 2018.
8 OptimalSort, https://www.optimalworkshop.com.
9 C. Righi, J. James, M. Beasley, D. L. Day, J. E. Fox, J. Gieber, and L. Ruby, "Card sort analysis best practices," Journal of Usability Studies, vol. 8, no. 3, pp. 69-89, 2013.
10 V. Estivill-Castro, "Why so many clustering algorithms: A position paper," ACM SIGKDD Explorations Newsletter, vol. 4, no. 1, pp. 65-75, 2002.   DOI