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With funding from a National Science Foundation (NSF) IUSE/PFE: Revolutionizing Engineering and Computer Science Departments (IUSE/PFE: RED) grant, our vision is to focus on faculty development and culture change to reduce the effort and risk experienced by faculty in implementing pedagogical changes and to increase iterative, data-driven changes in teaching. Our project, called Teams for Creating Opportunities for Revolutionizing the Preparation of Students (TCORPS), is an adaptation of the “Additive innovation” model proposed by Arizona State University [1]. The Department of Mechanical Engineering at Texas A&M University has a long legacy of individualistic and---in many cases---a fixed mindset [2] approach to teaching with the expectation of top-down management of change. The goal of our project is to evolve the departmental culture to a bottom-up team structure where the faculty embrace an innovative mindset and extend an iterative build-test-learn method of the maker culture [3] that was formalized by the Lean Startup [4] approach. Faculty already have investigative and experimentation-driven processes in place for research and a keen understanding of data to support their hypotheses. We aim to leverage this preexisting strength and knowledge by extending it to the faculty-led, small-scale, iterative improvement of curriculum and pedagogy 

This work in progress (WIP) paper describes a National Science Foundation funded RED (Revolutionizing Engineering Departments) Adaptation and Implementation (A&I) grant focused on changing the culture of a large traditional mechanical engineering department at Texas A&M University (TAMU) and is an adaptation of the “Additive Innovations” model developed by Arizona State University in their RED project[1]. The TAMU RED project is focused entirely on culture change via faculty development, with the goal of shifting from a culture where teaching is secondary to research and courses evolve via sporadic, undocumented, individual innovations to a culture that recognizes teaching’s role in both faculty and student success and encourages a sustained process of incremental improvement and responsiveness to student learning through experimentation, measurement, and sharing. Two key levers in this culture change are (a) a faculty development series focused on innovation and data-driven change and (b) the creation of communities of practice[] or “soft wired’’ teams that support each other and sustain incremental change across semesters as faculty cycle in and out of courses. Ultimately, the goal of this project is to enhance a departmental culture in Mechanical Engineering where faculty regularly discuss current curricular effectiveness and are empowered to develop pedagogical innovations that enable all students and faculty to thrive. 

A comparative study is presented to solve the inverse problem in elasticity for the shear modulus (stiffness) distribution utilizing two constitutive equations: (1) linear elasticity assuming small strain theory, and (2) finite elasticity with a hyperelastic neo-Hookean material model. Assuming that a material undergoes large deformations and material nonlinearity is assumed negligible, the inverse solution using (2) is anticipated to yield better results than (1). Given the fact that solving a linear elastic model is significantly faster than a nonlinear model and more robust numerically, we posed the following question: How accurately could we map the shear modulus distribution with a linear elastic model using small strain theory for a specimen undergoing large deformations? To this end, experimental displacement data of a silicone composite sample containing two stiff inclusions of different sizes under uniaxial displacement controlled extension were acquired using a digital image correlation system. The silicone based composite was modeled both as a linear elastic solid under infinitesimal strains and as a neo-Hookean hyperelastic solid that takes into account geometrically nonlinear finite deformations. We observed that the mapped shear modulus contrast, determined by solving an inverse problem, between inclusion and background was higher for the linear elastic model as compared to that of the hyperelastic one. A similar trend was observed for simulated experiments, where synthetically computed displacement data were produced and the inverse problem solved using both, the linear elastic model and the neo-Hookean material model. In addition, it was observed that the inverse problem solution was inclusion size-sensitive. Consequently, an 1-D model was introduced to broaden our understanding of this issue. This 1-D analysis revealed that by using a linear elastic approach, the overestimation of the shear modulus contrast between inclusion and background increases with the increase of external loads and target shear modulus contrast. Finally, this investigation provides valuable information on the validity of the assumption for utilizing linear elasticity in solving inverse problems for the spatial distribution of shear modulus associated with soft solids undergoing large deformations. Thus, this work could be of importance to characterize mechanical property variations of polymer based materials such as rubbers or in elasticity imaging of tissues for pathology.