DataStory\u2122: an interactive sequential art approach for data science and artificial intelligence learning experiences

Daniel Maxwell,Sarah Meyer,Charlotte Bolch

doi:10.1186/s42862-021-00015-x

Daniel Maxwell, Sarah Meyer + Show 1 more

Open Access

https://doi.org/10.1186/s42862-021-00015-x

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Abstract

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.

Highlights

In many ways, the delivery of technical training—data science more recently—has not changed much over the years
Because the much broader question of effective STEM instruction has been studied extensively in the literature, a primary research question was first articulated in order to delimit the scope of this inquiry. It reads as follows: Does sufficient evidence exist in the literature to support a sequential art approach to data science and A.I. education? The team identified three additional, more specific sub-questions for the systematic literature review
A preliminary review of the search results revealed that few attempts have been made to fully integrate insights from these three domains into an innovative learning experience

Summary

Introduction

The delivery of technical training—data science more recently—has not changed much over the years. The traditional lecture reigns supreme, usually delivered with the aid of an unending parade of PowerPoint slides. The experience is often mindnumbing, as it has been for the authors of this article. This method of instruction is parodied by Ben Stein as he delivers an economics lecture in Ferris Buehler’s Day Off (1986). The students are quickly rendered comatose by the drone of Stein’s voice. The scene perfectly captures what learning looks and feels like on the technical education death star, the burned-out hulk of a once productive

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