Validating curriculum development using text mining

ABSTRACT

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.

KEYWORDS:

• Text mining
• natural language processing
• curriculum development
• hierarchical clustering
• data science
• machine learning

Acknowledgments

The author would like to acknowledge the support of the Australian Office of Learning and Teaching through OLT grant FS15-0252 and two anonymous reviewers for insightful feedback.

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Funding

Australian Office of Learning and Teaching through OLT [grant number FS15-0252].

Notes on contributors

Jason West

Jason West has over 25 years of both academic and industry experience. He received the PhD degree in quantitative finance from the University of Technology, Sydney. He has published two books and over 40 journal articles on the use of quantitative techniques for solving complex financial, energy and climate change adaptation issues.