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Characterizing Computational Adaptive Expertise

Abstract

Our research is exploring the role that computational and analytical abilities play in innovation, in the context of engineering design education. We are applying the learning framework of adaptive expertise to focus our work and guide the research. The model of adaptive expertise has been presented as a way of thinking about how to prepare learners to flexibly respond to new learning situations, which is precisely what students are expected to do in the context of developing design solutions. We focus on “computational adaptive expertise,” which we abbreviate CADEX, since a major portion of an engineering curriculum focuses on developing analytical and computational knowledge. Yet, students often struggle with applying or transferring computational knowledge in the context of design. The current paper presents an overview of adaptive expertise and relates this concept specifically to engineering design education. In addition, the paper presents an overview of the research plan we are presently using to study CADEX in the context of a senior level biomedical engineering design course.

Introduction

Several recent reports stress that the competitive advantage of the U.S. lies in its role as a leader in technological innovation1,2. These reports make statements such as “leadership in innovation is essential to U.S. prosperity and security”3 and “innovation will be the single most important factor in determining America’s success through the 21st century”1. These reports send a resounding message that engineering education in the U.S. needs to emphasize and develop knowledge and skills that are essential to innovation in a rapidly evolving technological society. From an education standpoint, there are many factors to consider in creating an environment that fosters and develops the ability to engage in technological innovation. For example, students need to develop cognitive abilities such as technical fluency in a domain, as well as the ability to approach problems from a multidisciplinary perspective.

Our study is investigating the role that computational and analytical abilities play in innovation in the context of a conceptual framework that has recently emerged in the engineering education literature: adaptive expertise. The model of adaptive expertise has been presented as a way of thinking about how to prepare learners to flexibly respond to new learning situations. The current conception is that developing adaptive expertise requires development along two axes: innovation and efficiency4. Specifically, we are studying adaptive expertise as it applies to how one flexibly uses computational knowledge in novel situations.

We focus on “computational adaptive expertise,” which we abbreviate CADEX, since a major portion of an engineering curriculum focuses on developing analytical and computational knowledge in specific disciplinary domains. However, many engineering courses teach these topics “in the abstract,” and place less emphasis on how to adaptively use computational knowledge. That is, historically engineering curricula have been based largely on an “engineering science” model where engineering is taught only after a solid basis in mathematics and science5,6. Furthermore, engineering courses often focus on reductive thinking and “require

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McKenna, A., & Linsenmeier, R., & Glucksberg, M. (2008, June), Characterizing Computational Adaptive Expertise Paper presented at 2008 Annual Conference & Exposition, Pittsburgh, Pennsylvania. 10.18260/1-2--4415