literate employees across the world (Deloitte, 2018). And finally, a concluding section summarizes what has been learned so far.įindings from Deloitte’s survey of executives’ knowledge of cognitive technologies and artificial intelligence indicate the accelerating need for A.I. Section three briefly discusses what was learned from the scoping review, and section four introduces the first DataStory™ prototype as well as findings from a focus group study in which participant reactions to this new type of learning experience were recorded. The purpose of the scoping literature review is to discover what research has been conducted within the area of sequential art and data science and A.I. education is presented, followed by a second which describes the scoping literature review conducted by the research team. In section one, the rationale for a new approach to A.I. The story told here consists of five parts. In this article, a report of our initial search for answers to these questions is presented as well as interactional findings from the development team’s first DataStory prototype. Sequential art “refers to a number of sequentially juxtaposed abstract images that focus on form and technique, which may elicit from the viewer an aesthetic response, a notional sense of narrative and/or a possible theme”(Tabulo, 2014, p. So, is there a solution? Could a sequential art approach to data science education launch us in a new and more productive direction? Comics artist Will Eisner coined the term “sequential art” to describe art forms that use images deployed in a specific order for the purpose of graphic storytelling (Eisner, 2008). The way in which basic technical content is presented, however, is often technical and abstract, far removed from the mind’s preference for information delivered in a story format. is taken and includes foundational statistical ideas such as regression and probability that act as gateway ideas into the field (Russell & Norvig, 2009). In this article, an expansive definition of A.I. Admittedly, artificial intelligence is a broad field, encompassing machine learning, deep learning, and a wide variety of data management tools and techniques. The need to escape the technical education death star is especially acute in the field of artificial intelligence (A.I.) education. The scene perfectly captures what learning looks and feels like on the technical education death star, the burned-out hulk of a once productive sun where all enthusiasm and motivation for learning quickly fizzles. The students are quickly rendered comatose by the drone of Stein’s voice. This method of instruction is parodied by Ben Stein as he delivers an economics lecture in Ferris Buehler’s Day Off (1986). For students, the experience is often mind-numbing, as it has been for the authors of this article. The traditional lecture reigns supreme, usually delivered with the aid of an unending parade of PowerPoint slides. In many ways, the delivery of technical training-data science more recently-has not changed much over the years. 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. 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. education? The learning science, sequential art, and dual coding literature bases were then interrogated to answer that question. 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. In this paper, we propose a sequential art approach that uses visual storytelling with integrated coding learning experiences to teach data science concepts. 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. 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.
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