Last modified: 2018-11-30
Abstract
Data has become an increasingly important asset to businesses because of the valuable insights that can be gained through analysis. Demand for employees with expertise in business analytics will continue to rise in the foreseeable future. However, traditional business curricula typically do not adequately prepare students to perform data analytics for decision making. Colleges and universities can help students develop their data analytics and visualizations skills by integrating this content into existing courses such as accounting and finance, or by creating stand-alone courses.
This paper discusses how business instructors can design an effective introductory data visualization course by utilizing an integrated course design model, providing students with an opportunity to master all levels of Bloom’s cognitive taxonomy. We accomplish this by describing the course design structure and assessment methods used in a graduate-level data visualization accounting course at a large public university in the Northesast region of the United States. The pedagogical methods described in the paper include traditional lecture, active learning techniques, out-of-class reading and writing assignments, summative assessments through multiple choice exams, project-based assignments, and peer learning. This paper also describes the training and teaching resources available for instructors interested in teaching a data visualization course. We conclude by reporting the results of student feedback surveys, pre-test and post-test scores, and final projects as part of a quality assurance initiative to determine whether or not course learning objectives were met. The results suggest the integrated course design method was effective.
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