Preparing undergraduates for visual analytics

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Abstract
Visual analytics (VA) combines the strengths of human and machine intelligence to enable the discovery of interesting patterns in challenging datasets. Historically, most attention has been given to developing the machine component—for example, machine learning or the human-computer interface. However, it is also essential to develop the abilities of the analysts themselves, especially at the beginning of their careers. For the past several years, we at the University of British Columbia (UBC)—with the support of The Boeing Company—have experimented with various ways of preparing undergraduate students for VA. Although inspired by the need to prepare students to become visual analysts, the result turned out to be fairly general in scope, applicable to other analytical approaches, as well as more general research. In hindsight, this makes considerable sense. Although the visual component of VA is necessary, it is insufficient; many analytical activities at the human end involve nonvisual skills, such as effective decision-making and the ability to quickly focus on the relevant parts of a problem. The result of this experimentation is a third-year undergraduate course titled Cognitive Systems 303 (COGS 303) that focuses on “VA unplugged”—that is, on developing investigative abilities prior to training on the VA systems themselves. It was felt that if students focused on developing basic analytical habits of thought prior to learning VA systems, these habits would be reinforced by subsequent practice on “live” systems.
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Archival date: 2022-05-08
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