Data Analytics Research-Informed Teaching in a Digital Technologies Curriculum

Jing Lu ()
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Jing Lu: Department of Digital Futures, University of Winchester Business School, Winchester SO22 5HT, United Kingdom

INFORMS Transactions on Education, 2020, vol. 20, issue 2, 57–72

Abstract: In the business environment, the goal of data analytics can be characterized as improving decision making and its links to big data and other data-driven technologies. In UK higher education, degree apprenticeships are business-led and government-supported nationally recognized qualifications, where delivery is tailored to partner employer requirements. This paper focuses on the development of the data analytics specialism of the BSc Digital and Technology Solutions degree apprenticeship at the University of Winchester Business School informed by current research and practice. A data-driven analytical framework is first proposed to provide an overarching methodology for extracting knowledge and insights from (big) data. It covers key components of the analytics lifecycle from data management, data preprocessing, and integration through data modeling and business intelligence to insight management. Software tools related to collecting, cleansing, processing, analyzing, and visualizing data have been systematically discussed to provide the technological dimension. The methodology is then applied to the development of the specialist modules in data analytics, which represent the core thematic structure of the degree apprenticeship pathway. The culmination of the paper is an evaluation of the educational innovation of this digital technologies curriculum, highlighting teaching, learning, and assessment from the perspective of data-analytic thinking.

Keywords: business environment; information systems; data analytics; degree apprenticeship; digital and technology solutions; data-driven analytical framework; curriculum development; undergraduate specialist modules; research-informed teaching; analytical tools; data mining; business intelligence; insight management; decision support; big data (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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