NotesFAQContact Us
Collection
Advanced
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1146175
Record Type: Journal
Publication Date: 2017
Pages: 14
Abstractor: As Provided
ISBN: N/A
ISSN: ISSN-0958-5176
EISSN: N/A
Validating Curriculum Development Using Text Mining
West, Jason
Curriculum Journal, v28 n3 p389-402 2017
Interdisciplinarity requires the collaboration of two or more disciplines to combine their expertise to jointly develop and deliver learning and teaching outcomes appropriate for a subject area. Curricula and assessment mapping are critical components to foster and enhance interdisciplinary learning environments. Emerging careers in data science and machine learning coupled with the necessary graduate outcomes mandate the need for a truly interdisciplinary pedagogical approach. The challenges for emerging academic disciplines such as data science and machine learning center on the need for multiple fields to coherently develop university-level curricula. Using text mining, we empirically analyze the breadth and depth of existing tertiary-level curricula to quantify patterns in curricula through the use of surface and deep cluster analysis. This approach helps educators validate the breadth and depth of a proposed curriculum relative to the broad evolution of data science as a discipline.
Taylor & Francis. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Publication Type: Journal Articles; Reports - Research
Education Level: Higher Education; Postsecondary Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A