Using course‐subject Co‐occurrence (CSCO) to reveal the structure of an academic discipline: A framework to evaluate different inputs of a domain map

• Published in: Journal of the American Society for Information Science and Technology Vol. 68; no. 1; pp. 182 - 196
• Main Author: Hook, Peter A.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Periodicals Inc 01.01.2017
• Subjects: Academic disciplines; Algorithms; Clustering; data mining; Directories; Education; Indicators; information mapping; Intellectual Disciplines; Law; Law schools; Legal studies; Mapping; Similarity; Teachers; Topics; Validation studies; Visualization; visualization (electronic); United States > US
• ISSN: 2330-1635; 2330-1643
• DOI: 10.1002/asi.23630

This article proposes, exemplifies, and validates the use of course‐subject co‐occurrence (CSCO) data to generate topic maps of an academic discipline. A CSCO event is when 2 course‐subjects are taught in the same academic year by the same teacher. A total of 61,856 CSCO events were extracted from the 2010–11 directory of the American Association of Law Schools and used to visualize the structure of law school education in the United States. Different normalization, ordination (layout), and clustering algorithms were compared and the best performing algorithm of each type was used to generate the final map. Validation studies demonstrate that CSCO produces topic maps that are consistent with expert opinion and 4 other indicators of the topical similarity of law school course‐subjects. This research is the first to use CSCO to produce a visualization of a domain. It is also the first to use an expanded, multi‐part gold standard to evaluate the validity of domain maps and the intermediate steps in their creation. It is suggested that the framework used herein may be adopted for other studies that compare different inputs of a domain map in order to empirically derive the best maps as measured against extrinsic sources of topical similarity (gold standards).