This Research paper explores the activities within the biologically inspired design-focused engineering curriculum to determine if they fostered students' engagement in learning. This work builds on concurrent research exploring students' application of BID in engineering and teachers' implementation of BID within their respective engineering classrooms. Participants comprised ninth-grade high school students$(n=12)$enrolled in the first-year engineering course across two high schools. Qualitative content analysis was conducted on classroom observation field notes, student focus groups, teacher curriculum enactment surveys, and teacher interviews. The finding revealed that student engagement varied across the seven-week-long unit. In the initial week, engagement was relatively low since the activities were static and required learning to be scaffolded via worksheets. However, during weeks three through six, engagement positively shifted due to the activities being more dynamic, requiring students to engage in inquiry and design learning. Furthermore, students' academic engagement was fostered due to hands-on experiences and work-based authentic problems presented in the unit, which encouraged collaboration.

This innovative practice work in progress paper presents the Biologically Inspired Design for Engineering Education (BIRDEE) project, to create socially relevant, accessible, highly-contextualized biologically inspired design experiences that can be disseminated to high school audiences engineering audiences in Georgia and nationally. Curriculum units are 6–10 weeks in duration and will meet many standards for high school engineering courses in Georgia. There will be three curriculum units (one for each engineering course in the 3-course pathway), each building skills in engineering design and specific skills for BID. Currently in its second year, BIRDEE has developed its first unit of curriculum and has hosted its first professional development with 4 pilot teachers in the summer of 2020. The BIRDEE curriculum situates challenges within socially relevant contexts and provides cutting-edge biological scenarios to ignite creative and humanistic engineering experiences to 1) drive greater engagement in engineering, particularly among women, 2) improve student engineering skills, especially problem definition and ideation skills, and 3) increase students awareness of the connection and impacts between the engineered and living worlds. This paper describes the motivation for the BIRDEE project, the learning goals for the curriculum, and a description of the first unit. We provide reflections and feedback from teacher work and focus groups during our summer professional development and highlight the challenges associated with building BID competency across biology and engineering to equip teachers with the skills they need to teach the BIRDEE units. These lessons can be applied to teaching BID more broadly, as its multidisciplinary nature creates challenges (and opportunities) for teaching and learning engineering design.

This innovative practice work in progress paper presents Biologically inspired design (BID) to transfer design principles identified in nature to human-centered design problems. The Biologically Inspired Design for Engineering Education (BIRDEE) program uses biologically inspired design to teach high school engineering in a way that uniquely engages students in the natural world. For high school students, identifying natural systems' analogues for human design problems can be challenging. Furthermore, it is often the case that students focus on and transfer superficial structures, rather than underlying design principles. Based on the Structure-Behavior-Function (SBF) design ontology, we developed a modified cognitive scaffold called Structure-Function-Mechanism (SFM) to assist students and teachers with identifying functionally similar biological analogies and identifying and transferring design principles. In this paper we describe SFM and its importance in BID and our observations from teaching SFM to high school teachers during a multi-week professional development workshop in the summer of 2020. Based on teachers' work artifacts, transcriptions of discussions, and focus groups, we highlight the challenges of teaching SFM and our plans to scaffold this important concept for students and teachers alike.

Regularized iterative reconstruction algorithms for imaging inverse problems require selection of appropriate regularization parameter values. We focus on the challenging problem of tuning regularization parameters for nonlinear algorithms for the case of additive (possibly complex) Gaussian noise. Generalized cross-validation (GCV) and (weighted) mean-squared error (MSE) approaches (based on Steinfs Unbiased Risk Estimate. SURE) need the Jacobian matrix of the nonlinear reconstruction operator (representative of the iterative algorithm) with respect to the data. We derive the desired Jacobian matrix for two types of nonlinear iterative algorithms: a fast variant of the standard iterative reweighted least-squares method and the contemporary split-Bregman algorithm, both of which can accommodate a wide variety of analysis- and synthesis-type regularizers. The proposed approach iteratively computes two weighted SURE-type measures: Predicted-SURE and Projected-SURE (that require knowledge of noise variance Ð2), and GCV (that does not need Ð2) for these algorithms. We apply the methods to image restoration and to magnetic resonance image (MRI) reconstruction using total variation (TV) and an analysis-type .1-regularization. We demonstrate through simulations and experiments with real data that minimizing Predicted-SURE and Projected-SURE consistently lead to near-MSE-optimal reconstructions. We also observed that minimizing GCV yields reconstruction results that are near-MSE-optimal for image restoration and slightly suboptimal for MRI. Theoretical derivations in this work related to Jacobian matrix evaluations can be extended, in principle, to other types of regularizers and reconstruction algorithms.