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As educators we train our students to view the world using a particular disciplinary lens. In engineering this means helping our students to "think" like engineers. We teach them to categorize and solve problems using a technically focused mindset. For instance, they learn the importance of using hard data to quantify success or failure. Other disciplines, especially in the social sciences, focus additional attention on normative and substantive issues. Students are taught the importance of developing contextual understanding and of recognizing that lived experiences generate different perceptions of reality. This variety in discipline specific thinking gives rise to a rich diversity of ways to interpret the world. These mindsets, however, can also act like silos that prevent the exchange of information. For example, while engineers share a common language, they often find it difficult to explain to a non-specialist how they reached a particular decision. As teams are rarely composed of individuals from a single discipline, this presents a fundamental challenge. How do teams collaborate effectively across disciplinary boundaries?This paper, submitted as a work-in-progress, presents the current state of our course development. We discuss our learning outcomes, describe our pedagogical approaches, and identify areas of concern associated with this approach to multidisciplinary engineering education. By providing a detailed framework of the class as currently designed, we hope to solicit meaningful feedback from the multidisciplinary engineering community before teaching the course in the fall of 2017.

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Using a unique data set of causal usage drawn from research articles published between 2006–2008 in the American Journal of Sociology and American Sociological Review, this article offers an empirical assessment of causality in American sociology. Testing various aspects of what we consider the conventional wisdom on causality in the discipline, we find that (1) “variablistic” or “covering law” models are not the dominant way of making causal claims, (2) research methods affect but do not determine causal usage, and (3) the use of explicit causal language and the concept of “mechanisms” to make causal claims is limited. Instead, we find that metaphors and metaphoric reasoning are fundamental for causal claims‐making in the discipline. On this basis, we define three dominant causal types used in sociology today, which we label the Probabilistic, Initiating and Conditioning types. We theorize this outcome as demonstrating the primary role that cognitive models play in providing inference‐rich metaphors that allow sociologists to map causal relationships on to empirical processes.