This dissertation proposes, exemplifies, and validates the usage of course-subject co-occurrence (CSCO) data to generate topic maps of an academic discipline. CSCO is defined as course-subjects taught in the same academic year by the same teacher. This work is premised on the assumption that in the aggregate and for reasons of efficiency, faculty members teach course-subjects that are topically similar to one another. To exemplify and validate CSCO, more than 112,000 CSCO events were extracted from the annual directories of the American Association of Law Schools covering nearly eighty years of law school teaching in the United States. The CSCO events are used to extract and visualize the structure and evolution of law for the years 1931-32, 1972-73, and 2010-11—roughly, forty year intervals. Different normalization, ordination (layout), and clustering algorithms are compared and the best algorithm of each type is used to generate the final map. Validation studies demonstrate that CSCO produces topic maps that are consistent with expert opinion and four other indicators of the topical similarity of law school course-subjects. Resulting maps of the educational domain of law are useful as a reference system for additional thematic overlay of information about law school education in the United States. This research is the first to use CSCO to produce visualizations of a domain. It is the first to use an expanded, multi-part gold-standard to evaluate the validity of domain maps and the intermediate steps in their creation. Last but not least, this research contributes a metric analysis and visualizations of the evolution of law school course-subjects over nearly eighty years.
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|Commitee:||Henderson, William, Milojeviae, Staa, Plucker, Jonathan, Sugimoto, Cassidy|
|School Location:||United States -- Indiana|
|Source:||DAI-A 75/12(E), Dissertation Abstracts International|
|Subjects:||Law, Library science, Information science|
|Keywords:||Analysis, Data, Information, Mapping, Mining, Visualization|
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