以開放資料的教師學術專長彙整表為基礎之學科標準分類分析

Analyses of the Standard Classification of Fields Based on the Directory of Faculty Expertise Open Data

10.6120/JoEMLS.2017.541/0046.RS.AM

林頌堅(Sung-Chien Lin)

學科標準分類 ； 開放資料 ； 教師學術專長彙整表 ； 輪廓試驗 ； Word2Vec ； The Standard Classification of Field ； Open data ； The Directory of Faculty Expertise ； Silhouette test ； Word2Vec

教育資料與圖書館學

54卷1期（2017 / 03 / 01）

69 - 95

繁體中文

本研究以教育部提供的開放資料做為分析資料，利用學術專長文字資料的相似性，探討目前對大學校院系所進行分類所採用的學科標準分類，並提出改善的建議。使用的技術包括估計學術專長資料相似性的Word2Vec文字比對技術，以及分析分類性質的階層集群分析、多維尺度分析、輪廓試驗、近似學門分布和系所相似性統計等評估方法，並以資訊視覺化方法呈現研究的結果。研究結果指出目前的學科標準分類在分類結構、分類架構與資料品質上都必需要改善，才能符合教育統計、政策制訂與學術交流的需求。

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.

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