| Popis: |
This paper presents a series of analyses of the Standard Classification of Fields which was applied to the classification of all departments in universities based on measuring similarity between text data of the faculty expertise directory from open data provided by the Ministry of Education of Taiwan, and suggests some possible directions for improvement of the directory and the classification system. The analysis techniques included the Word2Vec text matching technique to estimate the similarity of faculty expertise, the methods to expose properties of the classification system such as hierarchical clustering analysis, multidimensional scaling analysis, silhouette testing, distribution of fields with similar expertise set, and statistics of the similarity between departments, and a variety of information visualizations to illustrate the analysis results. The results of this study show that in order to meet requirements from educational statistics, policy making, and academic exchanges, the organization structure, organization scheme, and data quality of the Standard Classification of Fields should be improved. |
