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3D printing (3DP) has been becoming pervasive in the K-16 education system. However, in many schools, new 3D printers arrive, work for a certain period, and before long break down due to lack of maintenance and support. It is therefore imperative for teachers to develop a deeper understanding of 3D printing in order to fully release its potential in engineering design. In this project, the course of engineering design for preservice teachers (PST, current undergraduate students) is developed and implemented with mechanical components from dissected 3D printers. The approach is to dissect a 3D printer’s hardware, explain each component’s function, introduce each component’s manufacturing methods, describe possible defects, and elucidate what works and what does not. This allows the PSTs to develop a better understanding of 3D printing process, have a better idea on how to fix a 3D printer when it breaks down, and design components that are compatible with 3D printing. The evaluation results show that the course was well received by the PSTs who have improved their knowledge in 3D printing. In the future course offering, both knowledge gain and efficacy will be evaluated to help us better understand the impact of the course. 

In 2019, University of Houston (UH) at Houston, Texas was awarded an NSF Research Experience for Teachers (RET) site grant titled “RET Site: High School Teacher Experience in Engineering Design and Manufacturing.” The goal of the project is to host 12 high school teachers each summer to participate in engineering design and manufacturing research and then convert their experience into high school curriculum. Given the experience from the first year’s operation and assessment, it was noted that the extant teacher self-efficacy surveys need to be further improved according to the specific needs of RET site. As such, an updated set of assessment tools was developed to evaluate the impact of RET site on high school teacher participants. In particular, a new teacher self-efficacy survey was created from synthesizing multiple sources including Bandura’s Instrument Teacher Self-Efficacy Scale, Collective Teacher Beliefs, and Teachers’ Sense of Efficacy Scale (Ohio State Teacher Efficacy Scale). Besides the new self-efficacy survey, more specific questions were added to pre- and post-summer self-reported questionnaires to better understand the teachers’ perception and receptance of the summer experience. Interviews were conducted individually instead of using a focus group. This allows the interviewee to be more vocal during the interview, allowing more in-depth understanding of their perception for future improvement. The new assessment tools were applied to the second cohort of 12 teachers in summer 2022. The assessment results show that the assessment tools were able to effectively capture teachers’ change in perception and evaluate the affective impact of the RET site. In the future, the tools may be improved and used in similar teacher professional development activities. 

Abstract Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads to missing relevant muscle activity, highly redundant data, complicated electrode placement optimization, and inaccuracies in classification algorithms. Here, we present customizable and reconfigurable drawn-on-skin (DoS) MEAs as the first demonstration of high-density EMG mapping from in situ-fabricated electrodes with tunable configurations adapted to subject-specific muscle anatomy. The DoS MEAs show uniform electrical properties and can map EMG activity with high fidelity under skin deformation-induced motion, which stems from the unique and robust skin-electrode interface. They can be used to localize innervation zones (IZs), detect motor unit propagation, and capture EMG signals with consistent quality during large muscle movements. Reconfiguring the electrode arrangement of DoS MEAs to match and extend the coverage of the forearm flexors enables localization of the muscle activity and prevents missed information such as IZs. In addition, DoS MEAs customized to the specific anatomy of subjects produce highly informative data, leading to accurate finger gesture detection and prosthetic control compared with conventional technology.