TThe objective of this engineering education project is to advance both basic and applied understanding of how to prepare engineering graduates to effectively and efficiently contribute to America's leadership in technological innovation. The PIs propose to study 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.
Adaptive expertise is an emerging area of research on learning that has shown promise in providing enhanced understanding of transfer of knowledge issues. It is a critical area of research that directly relates to U.S. global competitiveness through improving understanding of what is required to train innovative and efficient problem solvers who can transcend narrow disciplinary fields. The PIs plan to perform fundamental research with the intent to inform the practice of teaching and learning. They hypothesize that the general model of adaptive expertise can be applied specifically to characterize the attributes of efficiency and innovation in the context of developing CADEX. Therefore, they plan to focus on basic research to understand the nature of CADEX and to define the efficiency and innovation axes in terms of the underlying cognitive or affective attributes of each. This approach allows them to uncover how innovation and efficiency may be conceived more broadly, while at the same time enables us to define terms specifically in the context of CADEX. The advantage of combining fundamental and applied research is that the PIs not only identify cognitive aspects of computational adaptive expertise and potentially effective instructional strategies, they will also verify and test their findings in actual educational settings.
The research will benefit society by providing recommendations for instructional and assessment strategies to develop competencies required for technological innovation. These are essential skills in today's competitive economy and thus will enhance the United States' industrial competitiveness by producing graduates poised with cognitive strategies for developing efficient and effective design solutions.
This work introduced the concept of Computational Adaptive Expertise (CADEX) to specifically explore the nature of the computational, analytical, and representational knowledge required in the process of innovation. We focused on CADEX because we found that engineering students often do well in early stage design processes (defining the problem, generating ideas, researching the problem and competitive products, etc.) but struggle during the later stages where technical disciplinary knowledge is required to perform engineering prediction and analysis. The overall goal of the CADEX project was to characterize students’ and faculty’s conceptions of modeling in order to understand how these conceptions influence the teaching and learning of design, and how the important engineering skill of modeling can be better utilized in the process of developing innovative design solutions. Modeling is an inherent part in the engineering design process; however, our findings suggest that students often do not have very nuanced conceptions of the full power and use of models. When the modeling process is made explicit to students, they appear to obtain a clearer understanding of model-based reasoning in engineering design. By making modeling steps more explicit it helps students recognize the value of the predictive nature of modeling, and leads to changes in what is conceived as modeling. By focusing on the "computational" aspect of design we investigated ways to better enable students to develop fluency in adaptive modeling behaviors such as stating assumptions, creating appropriate equations more frequently, identifying the direction of the mismatch between model outputs and the physical situation, and recognizing limitations associated with abstract representations of a physical phenomenon.
