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Using Bloom’s Taxonomy to Support Data Visualization Capacity Skills
PROCEEDING

, Purdue University, United States

AACE Award

E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, in New Orleans, Louisiana, United States ISBN 978-1-939797-45-2 Publisher: Association for the Advancement of Computing in Education (AACE), San Diego, CA

Abstract

Data visualization skills are becoming a prerequisite for academic and professional success. There is a large demand nationally for information on how to teach data science and data visualization. Visualizing data is an iterative process of several stages. Output from one stage serves as input to another stage in the process. It is important that students not only understand the resulting output from the process, but also have an understanding of the process and the relationships between each stage. In this work an adaptation of the revised Bloom’s taxonomy is applied to data visualization process to aid instructors in designing instruction to target data visualization capacity skills and higher order thinking in the data visualization process. The Bloom in Data Visualization Tool (BiDVT) can be used to help instructors identify components of the process that students find difficult and develop learning assessment techniques in an undergraduate data visualization curriculum.

Citation

Byrd, V. (2019). Using Bloom’s Taxonomy to Support Data Visualization Capacity Skills. In S. Carliner (Ed.), Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 1039-1053). New Orleans, Louisiana, United States: Association for the Advancement of Computing in Education (AACE). Retrieved September 19, 2020 from .