Innovation and

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Non-Empirical Research

Published online November 17, 2021

Copyright © Innovation and Education.

DataStory™: an interactive sequential art approach for data science and artificial intelligence learning experiences

Daniel Maxwell1,Sarah Meyer2,Charlotte Bolch3

Research Computing, University of Florida, Gainesville, USA; Health Sciences Libraries, University of Florida Libraries, Gainesville, USA; Office of Research and Sponsored Programs, Midwestern University, Glendale, USA

Correspondence to:Daniel Maxwell

Received: January 4, 2021; Accepted: October 28, 2021

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Technical training in the fields of data science and artificial intelligence has recently become a highly desirable skill for industry positions as well as a focus of STEM education programs in higher education. However, most of the educational training and courses in data science and artificial intelligence are abstract and highly technical which is not appropriate for all audiences. In this paper, we propose a sequential art approach that uses visual storytelling with integrated coding learning experiences to teach data science concepts. A scoping literature review was conducted to answer the following question: does sufficient evidence exist in the literature to support a sequential art approach to data science and A.I. education? The learning science, sequential art, and dual coding literature bases were then interrogated to answer that question. With knowledge gained from this review, an initial DataStory™ prototype was constructed, using a technical platform capable of delivering an engaging and interactive sequential art learning experience. And finally, findings from a focus group study using the DataStory™ prototype are discussed in which participant feedback to this new learning experience is reported.

Keywords: Data science education, Statistics education, Programming language, Technical education

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