Identifying Topical Coverages of Curricula using Topic Modeling and Visualization Techniques: A Case of Digital and Data Curation

Digital/data curation curricula have been around for a couple of decades. Currently, several ALA-accredited LIS programs offer digital/data curation courses and certificate programs to address the high demand for professionals with the knowledge and skills to handle digital content and research dat...

Full description

Bibliographic Details
Main Authors: Seungwon Yang, Boryung Ju, Haeyong Chung
Format: Article
Language:English
Published: University of Edinburgh 2019-09-01
Series:International Journal of Digital Curation
Online Access:http://www.ijdc.net/article/view/586
Description
Summary:Digital/data curation curricula have been around for a couple of decades. Currently, several ALA-accredited LIS programs offer digital/data curation courses and certificate programs to address the high demand for professionals with the knowledge and skills to handle digital content and research data in an ever-changing information environment.  In this study, we aimed to examine the topical scopes of digital/data curation curricula in the context of the LIS field.  We collected 16 syllabi from the digital/data curation courses, as well as textual descriptions of the 11 programs and their core courses offered in the U.S., Canada, and the U.K. The collected data were analyzed using a probabilistic topic modeling technique, Latent Dirichlet Allocation, to identify both common and unique topics. The results are the identification of 20 topics both at the program- and course-levels. Comparison between the program- and course-level topics uncovered a set of unique topics, and a number of common topics.  Furthermore, we provide interactive visualizations for digital/data curation programs and courses for further analysis of topical distributions. We believe that our combined approach of a topic modeling and visualizations may provide insight for identifying emerging trends and co-occurrences of topics among digital/data curation curricula in the LIS field.
ISSN:1746-8256